Hybrid multi-layer artificial immune system

ABSTRACT

The hybrid artificial immune system consists of three main layers, including a solution application layer that interacts with the environment, a solution generation layer that solves combinatorial optimization problems and a modeling layer that analyzes problems and presents solution scenarios. The system solves evolutionary multi-objective optimization problems in network computing, robotics, artificial neural networks, protein network modeling, evolutionary systems and evolutionary hardware.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application claims the benefit of priority under 35 U.S.C. §119 from U.S. Provisional Patent Application Ser. No. 60/958,466, filedon Jul. 7, 2007, the disclosures of which are hereby incorporated byreference in their entirety for all purposes.

FIELD OF THE INVENTION

The invention is in the field of bio-inspired computing. The presentsystem deals with immunocomputing and artificial immunology. Theinvention involves the field of metaheuristics, used to solvecombinatorial optimization problems, particularly evolvingmulti-objective optimization problems. The system is applied to networkcomputing, evolutionary systems and collective behaviors, includingcollective robotics, evolvable hardware, artificial neural networks andprotein network modeling.

BACKGROUND

Artificial immune systems (AISs) are computational systems that emulatethe operation of the biological human immune system (HIS). AISs are inthe computational problem-solving class referred to as metaheuristics.Metaheuristics categories are grouped into local search techniques,swarm intelligence, genetic algorithms and AIS. Each of thesemetaheuristics models is used to solve optimization problems.

The traditional AIS mimics the HIS. In the HIS, there are two mainimmune system subsystems. The first layer is the humoral immune systemin which collectives of antibodies perform specific functions toidentify and attack invading antigens. The second layer is the adaptiveimmune system, which identifies a new (previously unknown) antigen,develops a geometrically complementary model in order to defeat it andpasses on this knowledge to the humoral immune system in the form ofmemory or immunity. As a known antigen attacks the host, the humoralimmune system draws on the previous experience and then detects andattacks the new antigen by cloning antibodies. The HIS usesdifferentiated antibodies, including B cells, NK cells and T cells(memory, suppressor and killer T cells).

There are limits to the HIS. First, since it is manifest in adistributed network, it is limited to local search, with no potentialfor strategic planning. Its knowledge base is restricted to pastexperiences. Second, its response time is restricted. If an unusuallyaggressive antigen attacks the host, the HIS may not be prepared to wardoff the intruder before the host is defeated. Third, it takes time topass on the immunity from the adaptive immune system to the humoralimmune system in the form of memory. Fourth, the HIS is easily confused.For instance, it may attack itself, a phenomenon that is manifested asan auto-immune disease. Similarly, it may overreact and manifest as anallergy. Fifth, as the host gets weaker, the immune response mechanismis suppressed, which is hardly reliable. Sixth, the HIS's high thresholdfor identifying and attacking an antigen may result in a reaction thatis too late to be effective. Finally, it is possible to infiltrate theHIS and disable it.

The traditional AIS, drawn from the HIS to solve complex problems,abstracts the concepts of the HIS for application to computationalenvironments. In the AIS, the artificial humoral immune system isstructured as a distributed network in which information is passed toself-interested autonomous members of the collective. This layer isprimarily reactive, so that antibodies are propagated on-demand in orderto attack known antigens.

As the artificial adaptive immune system encounters a new antigen, itemulates the HIS in order to create a customized solution to a problemand then passes this solution to the artificial humoral immune system.The adaptive process involves learning new ways to solve problems posedby new antigens. In combination, the two layers of the traditional AISdevelop a coherent system to solve optimization problems.

The AIS model provides novel approaches to solve multi-objectiveoptimization problems. Other metaheuristic models have problem-solvinglimits. The local search, swarm intelligence and genetic algorithmmodels are limited to past experience; the AIS model, however, movesbeyond the reaction-centric limits of past information constraints inproblem solving. With the exception of the traditional genetic algorithmmetaheuristic, all of the metaheuristic models use memory in order tolearn and evolve new solutions.

Memory is used differently in each main metaheuristic model. With localsearch methods, memory is limited to the present analysis. With swarmintelligence methods, memory is passed between members in real time.

In the AIS, memory is passed unidirectionally from the adaptive immunesystem layer, which learns to solve the problem, to the humoral immunesystem layer, which applies the solution at the appropriate moment. Overtime, the AIS maintains libraries of antigen and antibody pairings. Inthis way, the AIS memory, in its abstract form of immunity, is passedfrom the adaptive immune system layer to the humoral immune systemlayer. One of the challenges that the present system solves with ahybrid multilayer AIS is how to provide global information to localsearch optimization problems.

Learning is performed by the traditional AIS primarily in the adaptiveimmune system layer. An experimentation process solves the problempresented by each new antigen. Future encounters with the same antigenproduce a catalytic result by triggering a cascade effect of antigens atthe humoral system layer. As the antigen is further encountered,immunity is further fortified. With each new encounter of the antigen,there are fewer time lags within the linear immunity process. Moreover,as the system is optimized, it is able to solve evolving, increasinglycomplex problems. The information from the custom solutions generated bythe adaptive immune system layer effectively restructures the humoralimmune system layer by requiring less cascade reaction in theperformance of the same antigen reaction function.

The traditional AIS solves problems by identifying, tracking andattacking antigens. While it can attack a known antigen, and develop adefense against a new antigen, it remains to be seen how it mayanticipate a potential antigen. An AIS can solve problems that emergefrom the HIS. For example, in an AIS, specific memory functions, such asthe allergic overreaction or an auto-immune dysfunction, may be blockedor suppressed.

As in the HIS, there are ways to assist the traditional AIS. First, avaccine provides a small dose of a specific antigen to allow theadaptive immune system layer to build immunity. Second, an artificialantibiotic helps the AIS to attack a specific antigen. Both modelsfortify the immune system defense mechanism.

Local search, swarm intelligence and genetic algorithms are useful forsolving bi-objective and multi-objective optimization problems. However,because of its ability to create custom solutions to new problems andpass them on as memory for future solutions to the same problems, thegeneral AIS may be used for evolutionary multi-objective optimizationproblems (eMOOPs) as well. In particular, solutions to eMOOPs are neededin complex computational combinatorial problems that involve changingand uncertain environments. In some cases, the trade-offs required inthe family of solutions to eMOOPs involve temporality and shiftingbiases. Such cases typically consist of an interactive process in whicha computational system is interacting with an indeterminate environment.

The present system provides important solutions in several categories ofapplications that involve eMOOPs. In particular, the present system isused for network computing, artificial neural networks, protein networkmodeling and evolutionary systems that involve collective behavior.

U.S. Pat. No. 5,440,723 (Arnold patent) addresses the “automatic immunesystem for computers and computer networks.” However, this patent, whichanticipates the major developments in autonomic computing in distributednetwork environments, is focused only on defeating an “undesirablesoftware entity such as a computer virus.” Similarly, U.S. Pat. No.7,093,239 (van der Made patent) follows the Arnold patent in focusing on“detecting unwanted code in a computer system.” Consequently, the Arnoldand van der Made patents seek only to emulate the performance of the HISin identifying and attacking viruses in the network computingenvironment. These first automated anti-virus computer developmentsprovide the groundwork for the present invention.

The novelties of the present invention, however, allow it to surpass thefocus on network security. The present invention uses the AIS as a majorcategory of metaheuristic to solve combinatorial problems, particularlycomplex eMOOPs, in a range of important network environments. Thespecificity of applications is detailed in this disclosure.

SUMMARY

The present invention consists of three layers: (1) humoral layer (Layer1), (2) adaptive layer (layer 2) and (3) anticipatory layer (Layer 3).While the present system discloses numerous novel methods for Layers 1and 2, the third layer is totally novel in this artificial immune system(AIS3). Layer 3 provides modeling and future predictions to the overallsystem. Taken together, these three layers present specific dynamicsthat have numerous advantages and applications that appreciably advancethe state of the art of metaheuristics in solving eMOOPs.

In Layer 1, a known antigen triggers the cloning of antibodies toproduce cascade effects in which antibodies are recruited in real timeto attack the antigen. Different types of antibodies in the collectiveare coordinated via the use of global information that increases theefficiency of their operations. Various methods are employed toaccelerate the time lags of the traditional linear immunity process.

In Layer 2, the encountering of new antigens compels antibodies tocreate a complementary mold to defend against the antigens. Thisinformation is then provided in real time to apply at Layer 1. Layer 2solves the immediate problem (antigens) and stores the solution inmemory to pass on the Layer 1.

Layers 1 and 2 provide information to Layer 3. At Layer 3, pathogens aremodeled and analyzed. Antigen mutation vectors, the cooperation ofantigens, the environment and the behavior of the AIS itself, aremodeled with multiple variables. Various modeling scenarios of potentialantigens are presented. The co-evolution between antigens and theenvironment is also modeled. The AIS3 itself is modeled in order tooptimize its performance in real time, particularly in relation toevolving antigens.

Layer 3 provides key insights to the operation of Layers 1 and 2. Byanticipating potential antigen behaviors and mutations as well asantibody solutions, the present system is able to dramaticallyaccelerate the problem-solving component of Layer 2 and theimplementation of the solution at Layer 1. Layer 3 modeling alsoprovides information to Layers 1 and 2 about re-organization processesso as to optimize their performance.

eMOOPs Problem Solving

The traditional AIS solves combinatorial optimization problems. However,these multi-objective optimization problems are generally static. Thesemulti-objective optimization problems (MOOPs) rarely involve changingenvironmental criteria. The adaptive immune subsystem addresses apresent problem in its current form and seeks to solve the problem inreal time. The main way for the traditional AIS to solve a problem morerapidly is to increase the rate of the cycles through the distributednetwork rather than to modify the problem-solving characteristics of itsalgorithms.

The biological HIS solves problems by using a local search approach in adistributed system. The adaptive immune subsystem solves problems inreal time by matching the antibody collective to the antigen geometricalconfiguration. The HIS then passes this information to the humoralimmune subsystem. When the system identifies a dangerous, recognizableantigen that is distinguished from the host, it triggers a cascadeeffect of cloning antibodies by accessing the memory of the priorantigen that was battled by the adaptive subsystem.

The AIS3 has several advantages in problem solving over these earliermodels. First, the AIS3 solves evolutionary MOOPs (eMOOPs). Because thepresent system has a modeling layer, it is able to assess, andanticipate, evolutionary problems. The AIS3 uses modeling to producevarious scenarios in probabilistic solutions with a range of horizons.This modeling aspect is critical in order to understand and analyzetransforming combinatorial optimization problems. With the AIS3,predictions about antigen performance are able to accelerate theperformance of the eMOOP's solutions at Layers 1 and 2. Not only are theproblems evolutionary, but the solutions are dynamic as well.

While traditional AIS approaches, and metaheuristics in general, uselocal or neighborhood search space, the present system develops a newconcept of search, namely, the notion of space-time search. This notionmore accurately reflects the temporal and evolutionary characteristicsof the dynamic problems. Temporal dynamics generally distinguish MOOPsfrom eMOOPs. Specifically, the notion of space-time search reflects theextensible geometrical transformation of configurations of objects overtime.

One way to represent eMOOPs is to analyze the combinatorial geometryinvolved in joining specific protein elements to create differentprotein types. In another example, the unique transformations ofchemical structures are represented as combinations of atoms indifferent stable states. In still another example, the uniquecombination of sub-atomic elements that comprise atoms will transform atkey episodes from one stable state to another. In all of these cases ofcombinations of changing physical properties, eMOOPs involve theextension of elements using combinatorial geometry in space-time.

The main way to emulate the eMOOPs is by using animation modelingprocesses. The search for solutions to eMOOPs requires a limitedmathematical search within constraints over time. The challenge tosolving eMOOPs is to realize not only that the problems are evolving,but also to realize that the constraints within which solutions arefound also change.

Assessing and testing combinations of solution options for eMOOPs usingevolving multiple constraints is the challenge of the AIS3. In general,solutions to combinatorial problems require production of a family ofoptions. Furthermore, because the eMOOPs' solutions transform in time,the range of options constantly changes as well.

One of the solution option constraints involves time itself. The systemhas time limits within which a problem must be solved. While the systemis evolving solutions, the antigen itself is continuing to evolve inorder to evade the system. If the system does not solve a probleminvolving an antigen within time constraints, the antigen will overwhelmthe system and it will crash. Thus, to solve problems within timeconstraints, it is important to establish an allocation of resources.

The problem-solving process itself proceeds in stages in the AIS3. Afterthe initial problem is solved as an outline, a deeper solution isprovided in a second phase, while a yet more detailed solution isprovided in a later phase of the solution development process. Theproblem-solving process involves updating and feedback through the wholesystem in order to improve the results of earlier solution attempts.

In particular, the criteria for solutions change over time as thecircumstances of the problem change. The criteria for the priorities ofthe selection process changes as the conditions of the problem change.The set of solution options changes as the trade-offs of two or moreconstraints change.

One of the advantages of the present system is the use of globalinformation in a distributed system to access and map local search spaceover time. The analogy from metaheuristics is adaptive memoryprogramming (AMP) used in local search techniques. In AMP, anaccumulation of knowledge is accessed as the metaheuristic proceeds overtime. After an initial solution is offered to solve an optimizationproblem, the search process proceeds to accumulate information that isuseful to supplement the solution until a more robust and adequatesolution is developed.

The present system uses Layer 3 to develop sophisticated models ofproblems from information supplied by the actual empirical experiencesof Layers 1 and 2 in order to develop a systematic set of solutions toeMOOPs. By using global information that is accumulated in memory andthat is accessible in the distributed system, the present systemprovides a novel set of techniques to solve eMOOPs. The accumulation ofinformation in memory provides a way for the system to learn.

Because information in the system is updated asymmetrically and accessedby antibodies at Layers 1, 2 and 3, the system develops a novel way toprovide social learning. Though data are input and accessed locally,they are generally only temporally local, because the information thatis accumulated is global. This information also includes data sets ontransformative problem solving in a dynamic environment. Furthermore,these solutions are modeled using animation simulations. In using thesesimulations, the sets of solutions that reveal the trade-offs ofmultiple constraints are represented as multiple scenarios.

There are several categories of constraints that, in combination,characterize MOOPs and eMOOPs.

For MOOPs, these constraint categories include:

Space (geometric extension and change-of-state space)Memory types (central memory, distributed memory and associative memory)Database architecture (central database, active database and distributeddatabases)Search types (local search, neighborhood search and global search)Learning types (accumulated learning and social learning)Data analysis (pattern analysis and distributed data analysis)Distributed nodes (scalability and size of network)Resource constraints (computational resource constraints, memoryresource constraints, logic and analytical resource constraints andcommunication resource constraints)Resource optimization (efficiency, minimal resources need to solveproblem)

For eMOOPs, these constraint categories include:

Temporality (space-time and evolutionary processes)Change of state (rate of change, periodicity of change, equilibrium anddisequilibrium)Game theory modeling (temporal simulations and discontinuous change)Collective behavior (relations with neighbors and relations betweenautonomous entities)Social learning (collective relationships between autonomous agents inlearning)Environmental feedback (symmetric, asymmetric and causes ofenvironmental stimulus)Endogenous feedback (endogenous stimuli and reaction timing)Data analysis (evolutionary data analysis)Restructuring modalities (deterministic, indeterministic and relationsof structure and function)Resource optimization (peak and off peak, routing optimization andscheduling optimization)Restructuring conditions (trigger transformation conditions, quality ofweights and threshold conditions)

ADVANTAGES OF THE INVENTION

The present system has a host of useful advantages. The inventionadvances the use of “memory” in distributed systems. Rather thanlimiting a distributed system to local memory, the present systemprovides several levels of actual memory use.

Due to its use of an enhanced memory system, the present system is alsostrategic. The AIS3 is viewed as a sort of “cognitive” system in itsinteraction with the environment. Specifically, the present systemprovides a novel model for social learning with the aim of solvingcomplex optimization problems.

The AIS3 system dramatically accelerates the problem-solving mechanismsof this type of metaheuristic. By adding an artificial layer to thetraditional AIS model that anticipates the actions of possible antigens,the present system provides a more rapid reaction to solve problems. Inparticular, Level 3 is useful in identifying, and optimizing, resourceconstraints. Consequently, the AIS3 model limits the overreaction aswell as the misdirection inherent in traditional AIS models.

The present system is also more flexible than earlier AIS models becauseit anticipates antigen scenarios. Thus, it is useful in a far broaderrange of applications than traditional models.

The present system is able to evolve solutions by integrating aspects ofall three layers interactively. This ability provides maximum efficiencyto problem-solving capabilities. The interaction of active andanalytical (modeling) problem-solving functions provides furtherperformance enhancements. The AIS3 system allows the application of thelearning process to complex evolving systems.

DETAILED DESCRIPTION OF THE INVENTION (A) AIS3 Layer 1

As in the traditional AIS, Layer 1 is the site for performance ofactions of antibodies to solve the main problem of defending againstantigens. Layer 1 is the active layer that uses the analyses performedat other layers and passed on to Layer 1 in the form of memory of pastsolutions. Layer 1 interacts with the environment to apply previouslydeveloped solutions. In addition, since it is actively interacting withits environment, Layer 1 provides the site for active experimentation ofproposed solutions from other layers to test if a proposed solutionactually works. The information that is obtained on active solutionsused at Layer 1 is then passed on to the other layers in the AIS3 sothat they may update their databases and develop new solutions.

(1) Swarm Intelligence in Distributed Collective of Autonomous Agents:Layer 1 as Active Layer and Interactive with Environment

Swarm intelligence is a form of metaheuristic that involves the sharingof information between multiple independent agents in order to solve aproblem. Ant colony optimization (ACO) uses external pheromones so thatindividual ants can communicate within the collective to accomplish atask such as foraging for food. Similarly, particle swarm optimization(PSO) is a metaheuristic which emulates a swarm of insects by shiftingthe leader of a collective whose members interact with their neighborsto obtain and share information to solve problems.

Layer 1 of the AIS3 uses a form of swarm intelligence called stochasticdiffusion search (SDS) in which the members of the collectivecommunicate with each other directly. In this case, the autonomousagents are specialist antibodies that work together in a division oflabor by making multiple passes through the system. The specialistantibodies have specific functions such as the ability to perform anoperation or to activate other specialist antibodies. Applying SDS tothe collective of specialist antibodies allows antibodies to communicatedirectly with other specialist antibodies as they are processed throughthe distributed network.

The autonomous antibodies at Layer 1 only have specific informationrelating to their particular functions and the ability to interoperatewith other specific antibodies. This communication system, by employingthe SDS metaheuristic model, allows direct, but limited, exchange ofinformation. In the context of an HIS, antibodies are proteins thatinteract in the distributed immune system network by providing signalsand seeking the appropriate binding fit to solve problems such asdefending the host from antigens.

Layer 1 works by organizing several phases of operation. In an initialposition, the antibodies patrol the distributed network to identifymalicious antigens. Once identified, antigens are attacked by differenttypes of cooperating antibodies. In most cases, the antigen is known andsolutions are easily applied. In those cases in which the hostidentifies new antigens for which past solutions are not available, thechallenge of finding new solutions is passed to Layer 2.

By employing the model of collective behavior, the AIS3 Layer 1simultaneously cooperates (by using the SDS metaheuristic for directcommunication between specialists) and interacts with its environment toapply solutions to problems that have largely been already encountered.In some cases, new solutions to new problems are also tested at Layer 1as well.

(2) Different Levels of Completeness of Cascade Effects forComputational Efficiency and Resource Optimization in DynamicEnvironment

Level 1 implements a cascade effect of generating and coordinating anumber of specialist antibodies in order to solve the problem ofattacking a known antigen. This cascade process is a key method oforganizing collectives of autonomous agents using a form of swarmintelligence. The information that the individual antibodies have islimited to access to a neighborhood region of antibodies. However, giventhe fact that the antibodies are in the distributed network in aconstant state of motion, they are constantly interacting with a rangeof specialist antibodies and using the SDS metaheuristic to obtain andshare information with other specialist antibodies.

The cascade effect of Layer 1 uses the clonal selection process in whichantibodies are generated from other antibodies on-demand. Once aspecific threshold is met, such as the identification of a knownantigen, information is passed to specific antigens which then generateother antibodies in order to apply the solution of attacking theantigens. To conserve resources, the antibodies perform cascades instages of escalation, when specific thresholds are met, in the form ofcascade escalation scenarios.

Once the solution has been applied and the antigens are reduced, theantibodies that were generated to apply the solution are removed and thesystem gradually moves to an equilibrium position.

(3) Social Learning: Network Collaboration in Distributed System, Bothwithin Layer 1 and Between Layers

The organization of the collective of antibodies in the AIS3 distributedsystem represents a form of social learning. Specifically, teams ofspecialized antibodies work together to share information and performspecific functions such as assessment of a problem (identification of amalicious antigen), application of a solution, confirmation ofsuccessful solution application and return to ordinary equilibrium statewith minimum resource burdens. The teams of antibodies work together byusing a division of labor and coexist in a heterogeneous distributedsystem in which they compete with other antibodies to achieve aparticular goal.

In order to rise to a social dimension, antibodies in the collectiveperform functions such as analysis, specific operation activation,interaction with other antibodies and interaction with the environment.By interacting with their environment, the antibodies perform a form ofexperimentation process with feedback that triggers a particularfunction once a threshold of action is satisfied.

The best way to achieve social learning in the network is for antibodiesto train together to achieve a specific objective, such as defeating aknown antigen. Once known antigens are detected, specialized antibodiesactivate a cascade process in order to apply known solutions tooverwhelm the antigens. Evidence from past experiences with similarknown antigens is used to rapidly train the antibodies.

Layer 1 conducts trial runs to defeat the antigens. The AIS3 applies theknown solutions to the antigens and obtains initial environmentalfeedback. If the solutions work, the antigens are defeated and the Layer1 antibodies recede to the initial equilibrium state. On the other hand,if the solutions do not work, a process of interaction with the antigensproceeds. In this case, Layers 1, 2 and 3 work together to generatesolutions to defeat the antigens. The new, or supplemental, solutionsare applied, and the system then obtains feedback from the antigens toascertain the effectiveness of the proposed solutions.

This process is a form of social learning because it incorporatesaspects of information exchange, environmental interaction and feedbackand solution revision; all of these aspects involve theself-organization of multiple agents in a distributed network. Once asolution is tested and confirmed, the Layer 1 antibodies return to theinitial equilibrium state in order to conserve resources.

(4) Danger Theory Application to AIS: Key Threshold of Behavior asSpecified Before Action and Requirement of Antigen Threat Assessment

One of the key elements of AISs is the identification of a distinctionbetween self and non-self by antibodies. If an antibody cannot make thisdistinction, it may attack the host after confusing the host with aninvader. Similarly, an AIS's antibodies need to identify invadingantigens, which are non-self.

Danger theory is applied to AISs when invaders that stress or kill cellsinduce signals which hence allow their identification as dangerous.Establishment of criteria to activate the AIS3 is critical for optimaloperation. The AIS cannot be deceived by endogenously derived signalswhen it must be activated by inputs from exogenously derived signalsgenerated by hostile antigens.

The AIS3 is activated by satisfaction of specific thresholds which areclarified before inducing an action. While the self/non-self distinctionis important, it is also important to develop a coherent method toactivate antibody behaviors after a threshold is satisfied in order toattack hostile antigens. The specific threshold is used initially toidentify the hostile antigens and then to test known solutions at Layer1. This threshold limit must be set at a high level before theinitiation of an attack by aggressive antibodies that will drainvaluable system resources. After obtaining feedback from the networkabout the progress of the applied solutions, the system will eithermodify its use of antibodies or will limit its response and return tothe initial equilibrium state.

Danger theory is not applied to all cases in the same way. For instance,there are degrees of hostility of antigens as well as degrees ofhostility of responding antibodies. The response to some mild antigenswill be mild in order to conserve scarce computational resources, whilethe response to some aggressive antigens will be correspondinglyaggressive and escalating. In nature, not all host bodies are in thesame condition; weaker hosts will not be able to fend off aggressiveantigens as rapidly as stronger hosts. Similarly, the efficiencycriteria of computational economics require that degrees of danger beidentified so that resources may be allocated proportionately. As moreinformation becomes known about a particularly hostile antigen, the AIS3will allocate more capabilities to solve the problems. A relativelybenign antigen, on the other hand, will not require intensivecomputational resources.

In time, the AIS3 will evolve a stochastic model to assess the relativedanger from the initial contact with an antigen. After first runningthrough the database library of known antigens, Layer 1 producesantibodies to match the antigen, proceeds to apply known solutions andobtains feedback to assess the relative effectiveness of the solutions.The evolutionary development of this process produces an outcome thatassesses danger from the viewpoint of statistical relevance, with themost statistically probable assessment and outcome generated from Layer1 experience. New experiences add to the database library and continueto expand and modify future probabilities.

In another embodiment of the system, exogenous data provides informationto the AIS3 in order to develop an assessment of the conditions forspecific antigens and their prospective solutions. Multiple externalenvironmental variables are assessed in terms of providing theconditions for antigens to survive. Danger theory assesses theseconditions and the system adjusts the thresholds of activation ofantibodies accordingly. These external conditions may be the keydetermining factors that inform the AIS3 of the nature of the antigenthreat for which Layer 1 must prepare and attune its activationthreshold.

Layer 1 possesses both the antigen identification function, whichemploys danger theory, and the active and interactive functions ofapplying and assessing solutions.

(5) Greedy Antibodies in Exogenous Ecosystem: Competition of IndividualAntibodies in Cooperative System that Distinguishes Between Self/Otherto Solve Problems

The main unit of operation in the AIS3 is the antibody, employed by thesystem in collectives. The antibodies compete among themselves, yet,collectively, work together cooperatively as well. In the AIS3ecosystem, each individual antibody is autonomous and “greedy”, that is,self-interested.

In natural biological immune systems, antibodies are “trained” at thefirst level of the humoral immune system such that they will “mature” inthe host so as to learn to distinguish between foreign invaders. Asobserved above, it is crucial for antibodies to recognize the self/otherdistinction—also referred to as major histocompatibility complex (MHC)in the HIS—in order to detect the level of danger of antigens and toactivate collectives of antigens against intrusions.

In the AIS3, groups of specialized antibodies work together collectivelyin order to perform specific functions. Specific teams of antibodieswill coordinate an attack on specific antigens. Once activated, theantibodies collaborate and cooperate in order to attack the antigens,yet each antibody is an autonomous entity. The competition between theantibodies is coordinated by the specialized functions of each antibodytype. When the most aggressive antibodies are generated once a keythreshold of activation has been satisfied, they are applied to solvethe most difficult problem of attacking intransigent antigens. Byworking together in a specialized way, the various types of antibodiesperform their collective functions to maximize system benefits.

One way to set the priorities of the various antibodies to achievecommon system goals that benefit the whole system is to offer rewardsand penalties. In particular, antibodies in the system have an aversionto penalties. In the biological HIS, the most aggressive antibodies areonly summoned contingent upon a high activation threshold, because theirgeneration has a high cost of resource consumption; when they are used,the host's temperature rises because their system is taxed by theantigen infection. Similarly, in the case of the AIS3, theidentification of hostile antigens requires the development of a seriesof steps to attack them by calling upon the various types of antibodiesin successive attempts.

The system thus maintains equilibrium between both system elements: (1)the self/other distinction to generate self-interested autonomousantibodies on-demand that will not attack the host but will ratheridentify and attack antigens and (2) cooperation between autonomousspecialized antibodies to efficiently perform the tasks of attackingantigens with minimal resource expenditures.

(B) AIS3 Layer 2

Layer 2 addresses real-time problem solving. The problems that are notsolved at Layer 1 are passed on to Layer 2. Specifically, problemspresented by new antigens are solved at Layer 2.

In the HIS, the adaptive immune system solves problems by developingspecialized antibodies that “complement” the geometric shape of the newantigen. The new solutions are then passed on to Layer 1 so thatsubsequently identified antigens are attacked by using these newsolutions. Layer 2 is referenced to as the adaptive immune systembecause it constructs a unique solution to a new antigen by adapting tothe antigen itself. Once the solution is passed on to Layer 1, immunityto the new antigen is established. In the traditional AIS, Layer 2solves a new problem with a new solution and passes on the solution toLayer 1 for future encounters in the form of a simple memory.

In the AIS3, Layer 2 addresses a class of complex optimization problemscalled evolutionary multi-objective optimization problems (eMOOPs).These problems are not generally solved at Layer 1 and require novel andcreative solutions. Layer 2 therefore constantly evaluates and solvescritical combinatorial problems at the frontiers of the system's abilityto solve problems.

Layer 2 works with Layer 1 because proposed solutions to new problemsare tested at Layer 1; Layer 2 therefore receives information on theefficacy of its solutions from Layer 1. In addition, Layer 2 works withLayer 3 to assist in the modeling of solutions to problems. Whereas thetraditional AIS ultimately solves problems within constraints, the AIS3provides methods for accelerating the generation and application ofsolutions as well as more complex solutions to more complex eMOOPs thanthe traditional AIS. Layer 3, for example, assists Layer 2 in the earlydetection of antigens.

The evolutionary character of the AIS3 and the environment thatgenerates the antigen problems provides a robust challenge for Layer 2to create novel solution candidates for novel eMOOPs. The existentialchallenge for solving problems in real time falls to Layer 2, which ison the front line in the battle with novel antigens. Once Layer 1applies solutions to hitherto-known antigens, the main challenges of themost difficult battles with new antigens would have been over becausethe initial solutions were discovered at Layer 2.

Layer 2 uses a range of processes to solve eMOOPs, includingsurveillance, diagnostics, experimentation, geometrical combinatorialoptimization, solution generation, solution testing, training ofantibodies with antibiotics and passing the solutions to Layer 1 in theform of highly developed memory.

(6) Layer 2 Devises Mechanism to Adjust Ais to Antigen HypermutationSequences

Layer 2 develops mechanisms to adapt to new antigens. In some ways, thecharacterization of the role of Layer 2 is to fight a war between thehost's immune system and constantly changing antigens. In order todefeat the host, the antigens engage in complex evolutionary processesin which they mutate their protein structures. Newly mutated antigenprotein structures will confuse Layer 1 of the immune system whichrelies on pre-defined solutions.

In order to defeat the rapid mutation-generated changes in the antigens,Layer 2 of the AIS3 adjusts its range of antibody mutations. Itaccomplishes this goal by combining new mutations of antibodies. Afterit creates new combinations of antibodies, it compares the combinedmutations to the newly discovered and mutated antigens to assess thelevel of success in solving the problem of defeating the antigens. Layer2 then adjusts its rate and degree of antibody mutation to match theevolution of the antigens until it meets its goal of defeating theantigens.

(7) Evolution of Layer 2 Memory Mechanisms: Libraries of ProblemSolutions as Genealogical Record

Layer 2 uses a memory mechanism to collect, access and transmitinformation to and from other layers in the AIS3. In the HIS, memoryfrom the adaptive immune system is stored as immunity to an antigen,after a solution to defeat a newly discovered antigen is found bydeveloping a geometrically complementary combination of antibodyproteins. The unique geometrical combination of antibody proteins isthen remembered and accessed by the humoral immune system when the sameantigens are discovered so as to better prepare a rapid response to ahostile invader. In traditional AISs, the memory mechanism emulates theHIS by creating a distributed network with autonomous agents interactingwith local search processes.

Rather than being limited to local search protocols, the AIS3 system,contrastively, uses global information in local search. This noveladvancement of the art provides a range of advantages. Particularly, thegeometric combination of antibody proteins and the experimentationprocess used to discover each unique configuration are stored in acentral memory that is accessible to all three layers. Information fromany layer can be stored and accessed by any of the other layers at anytime. This process of memory storage and access dramatically acceleratesresponse times by providing a storage library of past experiences withsolving complex problems. Use of this memory system allows anaccumulation of experience that facilitates better preparation ofsolutions for successive rounds of problem solving.

With use of the present memory system, relevant past generations ofproblem solving are drawn upon and analyzed for solving presentproblems.

(8) Artificial Antibiotic Fortifies and Directs AIS3 Antibody Response

As in an HIS, antibiotics are applied to help the immune system createantibodies to fight a particular antigen. In the AIS3, antibioticsfortify different specialist antibody types by activating the generationof a specific antibody. By activating specific antibodies, the systemrapidly generates an antibody response to a particular antigen. Ineffect an artificial antibiotic stimulates a system-wide surge byactivating particular catalytic antibodies on-demand. Use of theartificial antibiotic “tests” the immune system to produce a desiredeffect. The antibiotic also fortifies a strained immune system byactivating specific elements at key times without taxing the wholesystem. An antibiotic may be applied at Layers 1 and 2 to stimulateantibody production.

(9) Artificial Predators in Ecosystem to Proliferate or RestrictAntigens

The conditions of the environment in which antigens proliferate arecritical to their existence. In biological systems, ideal conditions forthe proliferation of specific pathogens, such as bacteria, includeoptimal temperature, water and food source. In the AIS environment,antigens have optimal conditions that determine their robustness. Ifthese conditions are altered, the antigens will be weakened ordestroyed.

In biological systems, equilibrium of conditions maintains the survivalof antigens according to ecosystem dynamics. Removal of one variableresults in a species developing in a different direction and rate. Forinstance, if a natural predator is removed, the species willproliferate, while if a predator is enhanced, the species will die off.

In an AIS, the artificial ecosystem produces similar situations, with anequilibrium between specific species in the food chain. Removal of apredator for one hostile species will allow the species to proliferate;enhance a predator and the species will weaken. Similarly, if theconditions for a hostile antigen are changed, it is possible to changeits progress. Increasing the stress on the antigen by raising orlowering the temperature, for instance, weakens the species by placingit outside its optimal condition for survival.

Ecological dynamics are important to the AIS3 because the environment inwhich the antigens proliferate determines the conditions of success orfailure. By modifying these conditions, including exogenous conditions(temperature, water and food) and endogenous conditions (hostilepredators), the rate of change of the artificial evolutionary processesis modified and the effects on antigens change.

Understanding ecosystem dynamics is critical for Layer 2 to apply itssolutions to antigens because these exogenous dynamics determine theconditions for antigen structures and behaviors.

(10) Methods of Discovery: Constant Experimentation of New Pathways ofProblem Solution Based on Experience

In order for Layer 2 to solve complex problems, it must experiment tofind new solutions. In general, the first step is to compare the currentproblem to experience. The AIS3 accesses the central database library inorder to discover both prior solutions and the methods used to solveearlier problems.

Layer 2 then assesses the problem itself. By analyzing the antigen, theAIS3 evaluates data sets of the current problem by comparing the problemto earlier problems in the database library of problems and priorattempted solutions.

Layer 2 uses an experimentation process to generate solutions to eMOOPs.The solutions are tested, and ranked, with the successful solutionspreserved and stored in memory. As the solutions are tested by applyingsolution candidates to the antigens, feedback on the effectiveness ofthe solutions is provided. The system then evolves improved solutionsuntil the eMOOPs are adequately solved.

(11) Collective Teaching Processes to Pass on Global Information toFuture Generations

The present system transmits information globally in order forindividual antibodies to access information on-demand. Solutions toproblems are forwarded to the central database and then accessed byantibodies as they make passes through the distributed system. Not onlydoes the system provide social learning processes to collectives ofantibodies about solutions to specific experiences, but antibodies teachother antibodies directly about specific experiences. In particular,antibodies that solve specific problems pass on the information to teachfuture generations of antibodies. Use of information from pastgenerations of problem solving makes attempts to solve new problems moresuccessful.

While social learning is performed in the AIS3 as trial and error withmultiple antibodies seeking to solve problems, social training is adirected approach in which the solutions that are detected by thesuccessful antibodies are passed on to other, future, antibodies.Specifically, solutions from Layer 2 are passed on to teach antibodiesat Layer 1 for future problem solving.

In another embodiment of the present system, the system uses distributeddatabases to store and retrieve information about past experiences. Thismodel is particularly useful in a distributed network in which no onenode is dominant or centralized. In this case, the antibodies areconstantly moving through the system and accessing the next availablenode as they make their rounds. Information is passed on to the firstavailable node, which then interacts with and updates all the othernodes in the system.

(12) Geometric Typologies of Evolutionary Mapping Processes: AntibodiesMake Geometrically Complementary Replica of Antigen

In the HIS, antibodies in the adaptive immune system identify a newantigen and cluster on its surface in order to create a mold of itsunique configuration. This process identifies the binding sites thatallow the antigen to proliferate; by suppressing the binding sites, likefitting a key in a lock, the antibodies defeat the antigen and therebysolve the problem that the hostile invader imposes on the host.

Traditional AISs generally emulate this model of antibody collectivesworking together in the adaptive immune system to fit into the antigenin order to create a unique solution to attack the antigen. The limitsof the traditional AIS to local search methods of obtaining and sharinginformation, however, severely constrain the timing of the creation ofsolutions to the problem of the antigen. As in the HIS, if the problemsfor the host are not solved in time, the host will die (system crash).

In the AIS3, geometrical combinatorial optimization techniques areapplied to problem solving. After first identifying the antigen as a newtype of hostile invader, by comparing it with antigens with which thesystem has had experience and discovering that the present antigen doesnot conform to previous experiences, Layer 2 will begin the process ofanalyzing the antigen. A collective of antibodies spreads around thesurface of the artificial antigen in order to assess the uniquetopological characteristics of the antigen. The antibody collectiveassesses the antigen as a geometrical pattern-matching problem. Once itidentifies the unique contours of the new antigen, the antibodycollective re-combines in order to generate specific geometrictopological solutions over time.

In Layer 2, the antibodies recruit other antibodies to make a geometric“mold” of the antigen. The antibody mold is geometrically complementaryto the antigen. In effect, the antibodies generate a replica of theantigen. This geometric information is then analyzed and thecomplementary mold is used to defeat the antigen as quickly as possiblewith as few resources as possible.

To defeat the antigen, the AIS3 proceeds on two fronts. First, at Layer2, the system uses its complementary mold to produce antibodies thatpenetrate the antigen and disable its hostile capabilities. Second, atLayer 1, the system tags the antigen and produces a cascade effect thatattacks and engulfs the antigen until it is defeated.

(13) Reverse-Engineering Process to Pick Out Optimal Antigen Pathway

Antigens are continually evolving. The challenge of the AIS3 is toidentify the evolutionary vectors of antigen development and to generateantibody solutions in order to defeat the antigen within time andresource constraints. The evolutionary change in the environment thatgenerates the antigens' developmental pathway vectors provides thecontext for the AIS3 to produce solutions to defeat the antigens. Thechallenge of the present system is to identify ways to develop and tracksuccessful evolutionary pathways of antibodies that will defeat theevolving antigens.

The AIS3 produces an analysis of the antigens at Layer 2. This analysisis based on initial interactions between the system's antibodies and theantigens. The antigens are reverse-engineered in the antibody analysisby comparing the initial analysis of the antigens with prior experienceswith similar antigens. This comparison between different stages andtypes of antigens provides useful information about the evolution of theantigens. The possible pathways of antigen evolution are then analyzed,and this information is used to develop solutions to defeat the newantigen. Once a new solution is applied and is successful in defeating anew antigen, the data are recorded in memory for a future episode with anewer strain of antigen.

(14) Efficient Genetic Algorithms to Test Mutation Pathways of Antigens

Artificial antigens evolve in distinctive but predictable sequentialpatterns. These evolutionary processes use the main mechanism of geneticmutation in order to survive by defeating prospective host defenses. Thegenetic mutations of antigens produce specific pathways of evolution,the vectors of which may be tracked. In addition, the rate of antigenmutation is assessed.

Use of genetic algorithms (GA) is a valuable way to model the mutationsand pathway vectors (and development rates) of antigen evolutionaryprocesses. Genetic algorithms are computational entities that combinespecific genes to create a generation of solutions to fitness problems.The solutions are compared to the environment, and the most successfulsolutions are retained and combined in unique ways in order to generatebetter solutions for many generations until the problems are solved.

Narrowing the range of the antigen evolutionary pathway vectors by usingefficient GA processes makes it is possible to assess the limits ofantigen evolutionary processes and to test these processes against AIS3solutions. Once an antigen evolutionary pathway is assessed and tested,it is possible to recommend a solution to defeat the antigen.

A similar procedure is used to develop the mutation pathway vectors ofantibodies in the AIS3. After assessing the antigen mutation vectors andrates, the AIS3 analyzes and constructs antibody mutation processes inorder to match and defeat the antigens. This model of mutations inantigens and antibodies accommodates a complex view of thetransformation problems and evolution processes required to solveproblems.

The solution of an antibody (or antibody collective) for the problem ofan antigen requires finding the appropriate fit with the antigen'senvironment. An antigen can be examined on its own in the context of anenvironment unrelated to a particular host. The matching of a solutionto an evolutionary problem is performed by obtaining feedback on thesolution candidate from the environment. If a solution works, it isrecorded in memory and used again.

In general, the antigen mutation vectors are narrower with antigens thatare known and that are addressed at Layer 1, while the antigen mutationvectors are wider (and wilder) with new antigens that are addressed atLayer 2.

(C) AIS3 Layer 3

Layer 3 has several main modeling aspects. First, information aboutactual experiences is taken from Layers 1 and 2 in order to producemodel forecasts of both evolving antigen problems and antibodysolutions. Second, external data are used to model the environmentregarding possible antigen development. Third, possible antibodysolutions are modeled to be tested at Layers 1 and 2. Fourth, specificantigen evolutionary pathway vectors are analyzed and modeled in orderto prepare Layer 2 to produce solutions. Finally, antibody mutationdevelopment vectors are modeled for use at Layer 2 in order to solvenovel evolutionary problems.

The system uses game theoretic modeling to describe and analyze theopposition between antigens from the external environment and antibodiesfrom the host.

The invention uses a memory system to update and access informationabout its progress in solving complex evolutionary problems. Layer 3passes on recommendations discovered in its modeling and analyses toLayers 1 and 2, which data are accumulated in order to solve futurecomplex problems.

(15) Information from Actual Experience of Layers 1 and 2 Used forForecasting

The solutions that are applied at Layer 1 are accumulated in memory fromprior experiences at solving antigen problems at Layer 2. Layer 1 solvesproblems that have already been encountered by using known solutionsthat are accessed from memory. However, Layer 1 is also the site fortesting solutions from Layer 2. Layer 1 applies both known solutionsstored in memory as well as new candidates for solutions generated atLayer 2.

The feedback results from solutions generated at Layers 1 and 2 provideraw data for modeling at Layer 3. In particular, the approaches andalgorithms used to solve problems at Layers 1 and 2 are accessed in theanalysis of new and potential problems at Layer 3. This data reflectpast experience at solving problems, the successes of which are usefulin order to develop new solutions to complex problems.

(16) Game Theoretic Modeling of Possible Solutions to Test at Layers 1and 2

Layer 3 of the AIS3 produces model simulations of antibodies andantigens in order to produce solutions to eMOOPs. Modeling the antigensmakes the problems easier to analyze in order to develop and assesspossible solution candidates. Via modeling of the antibody collectives,complex solutions are developed that will solve complex problems.

The model simulations operate by narrowing the range of each problem'sparameters. The model tracks the evolutionary mutation pathway vectorsof the antigens and continually refocuses these parameters. The modelingprocess is able to manipulate variables in the antigen evolution processin order to assess the most efficient ways to attack the antigen andsave the host.

In most cases, the problem is time sensitive. That is, there are timeconstraints that must be overcome to keep the hostile antigen fromwinning the battle against the AIS3 and harming the host. Activation ofthe modeling process at Layer 3 indicates that Layers 1 and 2 could noteasily solve the problem. Consequently, Layer 3 is used in those casesin which the problems are difficult and require new tools. Inparticular, Layer 3 is activated in cases in which there is highvolatility of an environmental change that stimulates antigens andcreates a state of crisis within the host. This disequilibrium betweenthe host and the antigen is highlighted by large changes in the state ofthe environment in which the antigen is active.

Candidate solutions to eMOOPs are tested at Layer 1. If a solution(antibody collective configuration) kills an antigen and thus solves aproblem, then the system gradually achieves equilibrium by applying it.Another result of a proposed solution may be to only harm or slow thedevelopment of the antigen, thereby achieving another (manageable)equilibrium state. If the solution candidates are not successful atsolving problems, then this information is supplied back to the databaseand the modeling layer will continue to analyze the problem and supplymore solution candidates. This testing process continues until theeffective solution is identified and applied.

Candidate solutions to complex novel problems are also modeled at Layer3 and applied at Layer 2. Layer 2 solves new problems, the solutions forwhich are not available from accessing a database of prior solutions.However, Layer 3 has the advantages of simulating the solutions andapplying the solution candidates at Layer 1. These simulations track theperformance of Layer 2 and provide potential solutions that are notlimited to Layer 2's real-time interactions between antibody collectivesand antigens.

By analyzing past experiences, Layer 3 provides simulations of possiblesolutions that are applied and tested at Layers 1 and 2.

(17) Modeling Forecast Horizons and Probabilities of Horizons withScenarios

Whereas Layer 1 focuses on applying solutions derived in the past andLayer 2 focuses on the ever-present challenge of developing rapidsolutions to existing antigens, Layer 3 focuses on solving futurepotential problems. Layer 3 develops models that simulate futurehorizons. These forecasts are developed by analyzing the presentchallenges and generating potential solutions in the form of scenarios.The horizons of each potential scenario are limited by the quality andtimeliness of information and the degree of development of the antigens,the environment and the host's immune system. In general, the near-termforecasts provide a greater probabilistic likelihood of success thanlonger-term forecasts. Forecasts are continuously updated with newinformation so as to increase the likelihood of success in the shortrun.

The application of simulations in Layer 3 to solve complex eMOOPsindicates the anticipatory aspect of the AIS3. Layer 3 models numerousvariables in the antibodies, the environment, the antigens and thehybrid artificial immune system in order to develop a way to anticipatebehaviors and to efficiently arrive at solutions to complex problems.

(18) Anticipating Events in Immunological Process to Optimize EfficientStrategy for Applying Solutions

Simulations of antigen evolutionary processes and antibody collectivedevelopment provide forecasting tools in the form of probabilisticscenarios of behaviors. These models predict antigen behaviors. Themodeling simulations also provide valuable data to recommend efficientstrategies for antibody collectives to apply solutions to novelproblems. In effect, Layer 3 modeling is used to train the syntheticadaptive immune system, particularly at Layer 2. By providing modelingtools to anticipate events, Layer 2 is increasingly able to rapidlyadapt to the changing antigen mutation pathway vectors.

While it is recognized that co-evolutionary processes occur betweensynthetic antigens and the synthetic antibodies in the modeling process,the system provides concrete ways for the antibodies to prepare forsolving problems.

Layer 3's anticipatory capabilities allow the system to be pro-active.The system models not only synthetic antigens but also potentialantigens that prepare the AIS3 to produce synthetic antibodies. Afterthe anticipated antigens are identified and stored in memory, the AIS3pro-actively seeks out and attacks the antigens in real time. Thisapproach provides a dramatically more rapid response advantage relativeto traditional AISs and the biological HIS. In these approaches, theadaptive immune system must resolve the challenge of new problems inreal time, whereby the most intractable problems might destroy the host.In the present invention, a library of potential synthetic antigens andtheir solutions is accessed when a new antigen is immediately identifiedand the problem efficiently solved, thereby conserving valuableresources.

(19) Environmental Modeling System: Environmental Change and RapidMatching of Antibody Mutations to Antigen Evolution for Host Survival

Modeling the environment is preparatory to modeling the antigens and theAIS. The environment contains the conditions for the survival ofantigens. Understanding the antigens' ecosystem involves not justdetermining the conditions for survival but also incorporating theawareness that antigens interact with other species. Remove an antigen'spredators and the antigen flourishes; similarly, restrict the antigen'sfood source and the antigen is stressed. It is within this delicatebalance that data about environmental conditions reveal the optimalcircumstances for antigen survival.

In addition to the need to model the environmental conditions in orderto understand antigens, the model also simulates relations between theantigens. Multiple antigens interact, compete, cooperate and collaboratein order to survive. Inter-antigen dynamics are modeled by the presentsystem in order to demonstrate an accurate representation of exogenousbehaviors. Synthetic antigen ecosystem networks are modeled in thepresent invention. Multi-antigen modeling is necessary in order for ahost to prepare to defend against multiple simultaneous antigeninfections.

The present system provides antigen surveillance. Evidence is used totrack antigens external to the host AIS3. This remote antigen trackingevidence is used to develop the antigen model so as to assess possiblethreats to the host.

The environment is modeled in particular to assess rapid changes inequilibrium. Crisis periods tend to produce a spike in antigens. Forinstance, if a sudden temperature change rapidly escalates the numberand intensity of antigens, the host AIS3 must be prepared to respond.The trends in the exogenous environment are carefully monitored by Layer3. The analysis of these trends is used by the modeling system topredict the trajectories of the antigens. By anticipating the directionof the development of the antigens, the AIS3 is able to better toprepare responses and to solve the evolving problems.

The modeling of the external environment and the antigens is useful inorder to rapidly evolve synthetic antibodies and to match these possiblesolutions to the actual problems encountered by the antigens. Byanticipating the trajectories of the antigens, the AIS3 is able tooptimize the most effective solutions to guide the development ofantibodies.

(20) Co-Evolutionary Modeling: Co-Adaptation of Immune System Processesand Environment

It is difficult to understand the AIS modeling process withoutunderstanding the antigen and environment modeling processes. Thisinsight reveals that antigens and antibodies co-adapt. The antibodiesmust solve problems of antigen evolution because the price of notsolving the problem may be the death of the host. Yet, in order tosurvive in a host, the antigens continually mutate, staying one stepahead of the antibodies' evolution rate.

In a deterministic environment within equilibrium, the conditions forantigen adaptation are stable within the constraints of definableparameters. However, in an indeterministic environment, the AIS3 modelsthe exogenous system within a narrow range of future possible scenariosand forecasts the behaviors of antigens. Antigens develop at differentrates and in different evolutionary directions based on theenvironmental conditions and mutation pathway vector variability.

Antibodies in the host AIS3 will counter the evolution of the antigens.Their evolutionary developments mirror and exceed the mutation pathwayvectors of the antigens. This co-evolutionary game is modeled like aconstant war between rival tribes.

(21) Virus Modeling

In biological systems antigens consist of both bacteria and viruses. Theclass of viruses presents an interesting case for the modeling ofantigens in the present system because of their complexity.

Viruses are modeled in the present system by simulations that tracktheir mutation pathway vector trajectories and rate changes. Becauseviruses are geometrically extensible entities, combinatorialoptimization and evolutionary computation techniques are applied toanalyze their evolutionary mutation combinations as they are mapped outover time.

In particular, the hypermutation rates of synthetic viruses are analyzedin the present system. Hybrid genetic algorithms are applied to analyzethe mutation pathway vectors and rates. In the modeling process,artificial viruses are evolved by manipulating the mutation variables.In addition, viruses supply signals to the AIS3 in order to detectparticular hypermutation direction vectors and rates.

The present system establishes a typology of synthetic antigens. Bykeeping an inventory of artificial viruses, the system is far morelikely to solve problems rapidly because it has a frame of reference toassess new antigens. The system maintains not only a catalogue ofsynthetic antigen structures but also a library of solutions to pastantigen problems that are solved by antibody collectives. By maintainingeasily accessible inventories of both problems and solutions, the systemis better prepared to solve future problems as they are encountered. Thepresent system also develops active models of the viruses, beyond theirmere structures, in order to assess the probable trajectories of theirevolutionary potentialities. This complex modeling library is importantfor solving real and potential problems.

While game theoretic modeling is typically used to simulate specificcompetitive events between teams of agents, the present system also usesmodeling to simulate the cooperation and collaboration of collectives.On the antigen side, the present system models the cooperation of teamsof antigens. In some cases, antigens engage in symbiotic relationshipsto increase the probability of survival in hostile environments.

On the antibody side, teams of specialized antibodies work together andcollaborate to defeat the antigens as efficiently as possible.Particularly because they have different specialists and differentlevels of action, groups of antibodies compete among themselves bysupplying incentives and penalties in order to increase theeffectiveness of their collective mission. In this way, competitiveindividual autonomous agents will cooperate in a global system.

The present system uses modeling to simulate the experimentation processof viruses' evolutionary strategies. The system tracks the evolutionarytrajectories of the viruses and anticipates specific vectors. In somecases, the simulation will not disclose the virus strategy or willactively conceal the strategy in order to prepare an effective antibodyresponse.

Modeling simulations are used to test possible solutions to problems. Byadjusting the variables in semi-random ways, the model tests feedback inuncertain environments.

Once a virus is identified and modeled, the system passes on therecognition of these possible trajectories and forecast scenarios toLayers 1 and 2. Once Layer 1 is activated, a cascade of antibodiesenvelops and destroys the antigen. The present system also helps Layer 2better prepare for defeating novel antigens.

(22) Tag Targeted Antigen to Slow Evolutionary Rate

In another embodiment of the present system, a targeted antigen istagged by the AIS3. The aim of the tagging process is to slow theevolutionary rate so that the system may develop a defense to theantigen.

In the biological HIS, the humoral immune system will tag an antigen inorder to attract a collective of antibodies to the antigen for itsenvelopment and destruction.

The present system, however, tags antigens primarily to track theirdevelopment and to inform the modeling system about their evolution. Byactively modulating the antigen development rate, the antigens may benot only studied but also controlled. In fact, the present system willrun tests on the antigen by tracking its performance.

(23) Artificial Vaccines

The design, development and application of artificial vaccines areuseful features of the present system. By reverse engineering theartificial synthetic viruses, for example, it is possible to extractinformation that is useful in creating an artificial vaccine. Thevaccine is constructed of unique combinations of geometric elements ofthe virus.

The vaccine is input into the AIS3 in order to better prepare Layer 1and Layer 2 to perform. This is similar to training the system andleaving the system on a higher state of alertness. Vaccines behave asartificial boosters to highlight specific antibody features on demand.Use of vaccines fortifies specific elements of the AIS3. The use ofvaccines is particularly applicable when Layer 3 recognizes and expectsa future attack from an antigen; applying the vaccine just-in-timeprepares Layers 1 and 2 to respond to the actual presentation of theexpected antigen.

Modeling is used to reverse engineer a vaccine by simulating anartificial virus. The resulting vaccine is used to stimulate the AIS3 toactivate specific antibody functions. The main aim of creating andapplying a vaccine is to trigger the immune system operations, primarilyat Layer 1. By educating Layer 1, the system accelerates a response to areal antigen threat and thereby optimizes the system.

(D) Dynamics of Layers 1, 2 and 3

While Layer 1 represents the cascade effects of antibody collectives inthe humoral immune system and Layer 2 represents the problem-solving andlearning process of the adaptive immune system, Layer 3 represents theanticipatory process of the modeling system. The three layers areinteractive and dynamic. Layer 1 deals with applying past solutions andtesting new solutions, Layer 2 deals with solving new problems in realtime and Layer 3 deals with solving potential problems and developingfuture scenarios of problems and responses. Each of the layersrepresents a different line of defense against antigens and solvesincreasingly complex problems.

The three layers are coordinated. Information generated at Layers 1 and2 is input into the models of Layer 3. The problems that are solved atLayers 2 and 3 are stored in memory and are accessed for future problemsolving at Layers 1, 2 and 3. Each successive layer is used to solveincreasingly harder problems, with information obtained from thesesolutions available to share in future problem-solving encounters.

(24) Asynchronously Training Each Layer

Since each layer operates independently, each layer is trainedseparately. Layer 1 is trained by the experience of Layer 2 at solvingnew problems, the solutions of which are passed on as immunity (memory).Layers 1 and 2 are also trained by the theoretical calculations of Layer3, which anticipates and solves potential problems. Layer 3 is informedby data from Layers 1 and 2.

Though the three layers work together, the timing of each layer'straining is independent and asynchronous from the others, with dataentering all three layers at different times and different activationthresholds pertaining to each layer. Further, the different layersdemand different kinds of training. At Layer 1, the training is limitedto routine responses that are triggered by specific events. Although thetraining at Layer 2 draws on experiences from Layer 1 and analyses fromLayer 3, the training is completely original each time problem solvingis attempted. Finally, at Layer 3, the training is based on theconditions in the environment; with complex environmental conditions ofmultiple aggressive antigen hyper-mutation vectors, Layer 3 will model arange of solution option simulations much like a traffic controller.

Overall, the AIS3 layers cooperate to achieve a mission within timeconstraints by sharing information and acting in sequence to solveproblems.

(25) Combining Forecasting Scenarios and Memory

Memory is used to share information. Memories from past experience oranalytical models are stored in a central database or in distributeddatabases for access by all three layers. This accumulation ofinformation in a common memory provides the system with competitiveadvantages. In another embodiment, each layer will also have its ownmemory functions in order to accelerate its performance.

Layer 1 accesses the problem solving of Layer 2 in the form of immunity.By emulating Layer 2's experiences with creating precise solutions tothen-new antigens, Layer 1 draws on this memory when encountering asimilar antigen. Similarly, Layer 3's analyses and forecasts are used byLayer 1 to implement concrete solutions to projected problems and byLayer 2 to accelerate the performance of problem solving by includingforecast scenarios.

The AIS3 model unifies the three layers (using the temporal analogy ofpast, present and future) to solve eMOOPs rapidly within resourceconstraints.

(26) Learning from Most Recent Experience: Improving on Next GenerationSolutions with Projected Problem Solving

One of the advantages of the present system is that there are severalopportunities to solve problems. If the first layer cannot solve theproblem, there are other fail-safes that will attempt to solve theproblem. One advantage of this model is that there is improvement ateach layer as the system increases its problem solving resolution andadds new tools to meet the challenge. Escalation from one layer to thenext also has another benefit: new solutions are provided with freshevidence of recent successes.

The most recent experience of problem solving is also the most recentinformation provided in memory, with the highest quality of information,and is thus the first data accessed when seeking to solve a similarproblem. Particularly since antigen mutation vectors are generallynarrow, deviation from one set of solutions will not vary appreciably.As similar antigens are discovered, memory is accessed to providesimilar solutions. This memory-transfer function is typical in passinginformation from Layer 2 to Layer 1.

However, a totally new kind of antigen will require a new sort ofsolution unrelated to past solutions. In these cases, memory of pasteMOOPs and problem solving are of little guidance except for theabstracted qualities that allow a new problem and solution to beinferred from elements of the past. For these new problems, improving onnext generation solutions with projected problem solving is critical.Layers 2 and 3 work together to analyze the problem, to generatesolution candidates and to test the candidates.

(27) Pre-Immunity of Forecast Modeling Passed from Layer 3 to Layers 1and 2

Layer 3 performs modeling simulations on novel or potential problems inorder to generate novel solutions. Once an initial set of solutions isgenerated at Layer 3, solution candidates are presented to Layer 2 fortesting through actual interaction with an antigen. Specifically, Layer3 shows how an attack is expected. Layer 3 evaluates the timing of apossible attack by an antigen by modeling the antigen mutation pathwayvectors and consequently provides a critical method to help the systemplan.

Analyses from Layer 3 are also provided to Layer 1 for immediateimplementation of the plan to action. Though Layers 2 and 3 will worktogether to analyze and test new solutions, the solutions areimplemented for full-scale application at Layer 1. Immunity (solutionmemory) is then passed from Layers 2 and 3 to Layer 1. Analyses ofpossible antigens and antigen solutions are passed from Layer 3 as asort of “pre-immunity” by providing information that may be used inactual battles with antigens at Layers 1 and 2.

(28) Training Layers 1 and 2 with Modeling from Layer 3

In addition to providing memory between the layers, Layers 1 and 2 aretrained with information provided from modeling analyses at Layer 3.Analytical information alone is not sufficient to solve eMOOPs.Interaction between the AIS3 components, particularly at Layers 1 and 2,and the antigens (and the environment) is necessary in order to discoversuccessful solutions. This trial and error process is indispensable andprovides critical experience to the overall system. In general, Layer 1is the operational and active reactionary layer, though Layer 2 is alsoreactionary.

Information from all the layers usefully assists in the training ofLayers 1 and 2 in their actual interaction with antigens. However, thepurely analytical data sets provided by modeling simulations at Layer 3are particularly useful for assisting Layers 1 and 2 in their actualoperations. As the experiences of Layers 1 and 2 are carried out, Layers2 and 3 provide information on novel solutions to complex problems thatare used to train the first two Layers, with information from Layer 2training Layer 1 and information from Layer 3 training Layers 1 and 2.

This training process builds immunity as successful solutions areprovided to solve eMOOPs. This immunity is passed on to Layers 1 and 2for future action. The information is also passed on to Layer 3 in orderto supply data for future model simulations.

(29) Triggers of Layer 1 as Stimulated by Layers 2 and 3

Since Layer 1 is an active operational layer that directly interactswith the environment to solve problems presented by antigens, it isimportant to determine the conditions that trigger activities. Layer 1'scascade effects are stimulated by the satisfaction of specificconstraints, such as the identification of a hostile antigen. However,Layer 1 can be activated by Layers 2 and 3 as well. For example, Layer2's (problem solving) experience in combating a specific novel antigenis passed on to Layer 1 for activation when encountering a similarantigen. In this case, a pre-set trigger is constructed from Layer 2'sexperience in order to stimulate specific Layer 1 actions when a knownantigen is encountered.

Layer 3 develops pre-set criteria that automatically trigger a Layer 1cascade reaction when the conditions are satisfied by the identificationof specifications that were developed in the modeling of a potentialantigen. This triggering mechanism provides the AIS3 with a head startin combating a potential threat. In this case, the multi-scenarioforecast of an antigen is made by modeling simulations to identify thetarget window of probable activation. This information is then passed onto Layer 1 so that when possible variables are identified that fit theprofile of a hostile new antigen, the cascade process will be initiated.If the problem is not solved at Layer 1, it is passed on to Layer 2. Theinformation from both of these layers about the actual attemptedsolutions and environmental feedback from antigen interactions with thelayers is then provided to Layer 3 for further analysis and solutiondevelopment. This pro-active modeling at Layer 3 is used to betterprepare the activities of Layer 1.

(30) Bucket Brigade Sequence: Layer 3 Activates Layer 2 and Layer 2Activates Layer 1

The modeling functions at Layer 3 provide information to Layer 2 andstimulate specific activities in Layer 2 to solve eMOOPs. Similarly,information from Layer 2 is provided, through memory, to stimulatespecific functions at Layer 1. These relationships emulate the bucketbrigade model in which one process relies on another, which relies onyet another. In this case, the successive layers are not necessary forthe previous layer, but provide important analytical tools forsuccessively more difficult problem solving.

In some cases, the anticipatory functions of Layer 3 will activate theadaptive functions of Layer 2; likewise, the adaptive functions of Layer2 will stimulate the interactive functions of Layer 1.

However, the reverse is also true. When a problem is encountered, Layer1 is initially activated. If the problem cannot be solved at this layer,it is passed on to Layer 2 for solution. If the problem cannot be solvedat Layer 2, it is passed on to the Layer 3 for analysis and recommendedsolutions. If the problem cannot be solved by any of the layers withintime constraints, the host dies. Alternatively, the AIS may develop astrategy to fight an antigen to a draw. This goal creates a newequilibrium. Consequently, the AIS3 must evolve and adapt in order tosolve these complex problems because the problems themselves areevolutionary and adaptive.

(31) Horizon of Simulated Projections Limited by Information fromExperience of Layers 1 and 2

While Layer 3 does provide valuable analyses to Layers 1 and 2 in orderto assist them in solving problems, the information provided to Layer 3by Layers 1 and 2 is important as well. The raw data that are providedto Layer 3 are critical in establishing accurate assessments andsolutions in the form of simulated projections. The information from theactual experiences of Layers 1 and 2 is the source of the analyses atLayer 3. The model simulations are limited by the quality of the data.In particular, the horizon of simulated projections is limited by thesedata. The parameters of the simulations are restricted by the actualdata provided by prior experience.

Though Layers 1 and 2 provide information to Layer 3, Layer 3 goesbeyond this in constructing novel simulation scenarios of potentialantigens. This is important because the traditional AIS is particularlysusceptible to aggressive new antigens which it has not previouslyencountered. Nevertheless, information about antigens supplied fromLayers 1 and 2 are still a starting point in the modeling analyses.

(32) Access Library (Memory) from any of Three Layers

Information from actual encounters with familiar or new antigens atLayers 1 and 2 is input into a central memory. This data are accessedand used by all of the layers in order to identify prior experiences ofproblem solving from previous encounters with antigens.

In a distributed system, one way to perform this memory function is toprovide multiple sweeps through the system, each of which yields newinformation to record and access. For instance, the detection of aparticular type of antibody, which was generated in the Layer 1 cascadeprocess, implies that the cascade process is in motion at a particulartime. In this approach, memory is provided in a local searchenvironment, much like a commuter in a car that can only see those carsaround it as they are stuck in traffic, in the present system.

The availability of global memory in a local search, on the other hand,requires architecture of central memory that is accessible in adistributed environment. This is provided in the current system byduplicating and accumulating the new memories at specific junctions asthe antibodies reach specific points in the system while passing throughit multiple times. In effect, whole databases are copied and updatedwith most recent information and then passed on to the antibodies asthey repeatedly pass through the system. This model allows global memoryin a local search process and increases the amount of information thateach layer of the system possesses in real time. In a furtherembodiment, by limiting the data sets to immediately useful information,the amount of data is minimized and the antibodies can travel withlighter data storage loads.

(33) Parallel Operations of Layers 1 and 2 and Layers 2 and 3

Layers 1 and 2 emulate aspects of the biological HIS. The humoral immunesystem and the adaptive immune system solve problems with past and novelantigens respectively. These two layers work in tandem and executeproblem-solving functions by interacting with the environment to defeatevolving problems.

In the AIS3, Layers 2 and 3 also work in tandem. In these layers, novelevolutionary problems are solved initially at Layer 2. However, Layer 3generates multiple simulation scenarios regarding potential antigendevelopment that aid in the solution-testing process at Layer 2.

The operations of all three layers function simultaneously. Theinformation from all three layers is passed between the layers in orderto optimize their tasks. To facilitate these simultaneous operations,the layers are sequenced with multi-layer queuing that allows theinformation from one layer to be synchronized with the actions atanother layer.

This parallel and simultaneous functionality between the layers isparticularly important in order to solve multiple problems presented bymultiple antigens.

(34) Multi-Plasticity Dynamics

In the traditional AIS there are double plasticity aspects that activatea restructuration of the system once a threshold of behavior issatisfied. These plasticity aspects affect the architectural and theparametric adaptation components. For example, once a familiar antigenis identified by the host, the humoral immune system will restructureits configuration by launching a cascade effect. Once the threat haspassed, the system will restructure its configuration by returning to asteady state of equilibrium that limits the number and types ofantibodies patrolling the system.

In the AIS3 multiple plasticity dynamics affect the structure andoperations of the system. The third layer provides additional plasticitydynamics because of the temporal aspects of solving eMOOPs within timeconstraints. Layer 3 stimulates an alteration of the whole architecturein order to accommodate an additional layer of adaptability to complexevolving problems. Layer 3 enhances the ability of antibodies to becreated on-demand to modify the configuration of the overall system andthereby solve specific problems.

If the initial position is one of parametric plasticity (defined as anadjustable process that affects the overall system to change itsparameters while simultaneously performing an operation in order toadvance its effectiveness), then the addition of the modeling layer addsthe potential to modify the structure and function of the overall systemthrough analytical recommendations. In effect, the system learns throughthe execution of its various active, interactive and analyticalelements, and then transforms its structure to optimize its primaryproblem-solving functions.

In a further embodiment of the system, the analytical features of themodeling layer provide simulations about not only the AIS3 but also theenvironment and the antigens themselves, which simultaneously evolve,restructure and co-adapt to the AIS3. The exogenous ecosystem transformsits structure as it adapts. The co-evolutionary aspects of the constantrestructuring of the AIS3 and the environment containing the antigensproduce a new type of complex plasticity. It is critical to understandthe environmental plasticity conditions or the AIS3 will not be able toreshape itself in order to solve the most complex eMOOPs. The AIS3 mustmatch and surpass its rivals.

(35) Hybrid Genetic Algorithms Applied to Train AIS Layers and to GuideVirus Mutation Pathway Vector Simulations

Virus mutation pathway vectors are simulated at Layer 3. Hybrid geneticalgorithms are used to calculate the probable future scenarios ofmutation vectors. These algorithms assess the most efficient pathway formutation development. By employing these computational techniques, thesystem produces rapid solutions to complex problems. These evolutionarycomputation techniques apply to analysis of all antigens.

Once the antigens are analyzed, scenarios of possible pathwaytrajectories are produced, and solutions are generated, the analyticinformation is provided to Layers 1 and 2 for use in actual experimentswith antigen interaction.

Layer 3 models the antibody collective responses by using hybrid geneticalgorithms to design (a) customized mutation pathway vectors and (b)specific geometric combinations that will be successful in defeating theantigens. These evolutionary computation-informed analyses are criticalfor successfully solving the eMOOPs in real time. The hybrid geneticalgorithms are used to train the AIS3 for application primarily atLayers 1 and 2.

(36) Conservation of Resources: Computational Economics in the AIS3

Computational economics seeks to develop the conservation ofcomputational resources. The most efficient use of computationalresources provide the most competitive solution.

The present system faces a temporal constraint. If the AIS3 cannot solvea problem within time constraints, the antigen(s) will overwhelm thesystem. At the same time, the system needs to escalate itsproblem-solving functions in order to preserve scarce resources. Thisefficiency is integrated into the biological HIS since the first levelof defense is organized to resolve most problems by drawing on immunitymemory; more complex problems are resolved in the more sophisticatedadaptive immune system, which is rarely activated after its initialtraining.

A key goal of the present system is to obtain satisfactory solutions topresent problems within the computational constraints. To do so, Layer 1is activated when a familiar antigen is encountered. Multiple attemptsto solve the problem are then resolved by activating the other layersover time.

Despite the need to preserve efficiency of resources, in anotherembodiment of the present system, it is necessary to build someredundancy into the system. Without some redundancy, a particularmalicious antigen may overwhelm the system. However, with the ability toprovide sufficient resources for a multi-antigen attack, the systemincreases the chances of survival appreciably.

Artificial Neural Networks

A-NN is a class of artificial intelligence that emulates the operationof biological neural networks. In biological systems, clusters ofneurons are trained to perform a function and evolve a solution to newsets of problems based on this training. In the course of solving newproblems, the neurons reorganize, thereby creating an adaptive process.The AIS3 metaheuristic is applicable to several aspects of A-NNs. Itwill assist in the calculating and training of connection weights in anA-NN. It is also useful in the A-NN applications of pattern recognition,anomaly detection, data mining and search and digital imageclassification and alignment.

(i) Calculating Connection Weights

One of the main methods of solving problems with A-NNs is to engage in aprocess of learning which involves the restructuring of connectionweights between neurons. The effect of the change of neuron connectionweights is adaptation to a changing environment and facilitation of alearning process.

One way to calculate connection weights is to use the AIS3 model. TheAIS3 assesses the parameters of the environmental problem and adjuststhe connection weights accordingly by producing an initial solution atLayer 2 and modeling the problem at Layer 3. Tentative solutions aretested at Layer 1 and then improved with feedback from Layers 2 and 3.The AIS3 is involved with modeling the connection weights betweenneurons.

(ii) Training Connection Weights

A-NN training of neuron connection weights is performed by the AIS3. Thelearning process used in the AIS3 provides global information in a localsearch space thereby enabling the optimization of the training ofconnection weights. Memory is continually updated and shared by thelayers of the AIS3 which is useful to the learning process in the A-NN.In particular, the present system is useful for A-NNs that engage insolving problems in an evolving environment.

(iii) Pattern Recognition

Pattern recognition is a major A-NN application category. The AIS3 isuseful in optimizing the A-NN for pattern recognition as it addresses anevolving set of criteria by employing a modeling capability.

(iv) Anomaly Detection

While A-NN is applied to anomaly detection, the AIS3 optimizes theapplication by solving eMOOPs in real time. Anomalies are recognizedmore rapidly, and are even anticipated, by the AIS3-enhanced A-NN.

(v) Data Mining and Search

The AIS3 metaheuristic is applied to the A-NN application of data miningand data search. Data sets are analyzed by the enhanced A-NN sincepatterns are more rapidly recognized using AIS3 modeling processes.

(vi) Digital Image Classification and Alignment

A-NNs are applied to digital imaging for image classification andalignment. In image classification, A-NNs restructure to optimize theorganization, and reorganization, of image data sets. In imagealignment, digital sensors are optimized by A-NNs. In both cases, theA-NNs are improved by application of the AIS3 metaheuristic.Particularly in evolutionary environments with rapid change of multipleconstraints, the AIS3 is designed to enhance the A-NN performance ofimage classification and alignment challenges.

Protein Network Modeling

The present system is applied to protein network modeling. The maincategories of protein structure prediction, protein regulatory networkmodeling, functional protein modeling and artificial synthetic biologyare all simulated using the AIS3 metaheuristic.

(i) Protein Structure Prediction

In order to assess protein behavior, protein structures must bepredicted within specific conditions. This complex combinatorialoptimization problem is a major challenge to computational modeling. TheAIS3 metaheuristic is a useful tool in solving protein structureprediction problems. The AIS3 solves these eMOOPs by using both Layers 2and 3 to model and test the protein structure problems and to offeroptimization solutions within constraints.

(iii) Protein Regulatory Network Modeling

Protein regulatory networks are complex systems through which proteinsperform useful biological functions. Identifying these networkingoperations is a major challenge of biological sciences. The AIS3 isuseful in testing the protein regulatory network pathways. After firstusing the experimentation procedures of the AIS3 at Layers 1 and 2, theprotein regulatory networks are modeled at Layer 3. The model iscontinually improved upon as more information is obtained and tested.

(iv) Artificial Synthetic Biology

Synthetic biology is an outgrowth of recombinant DNA procedures in whichgenes are added or removed to achieve a desired man-made effect.Artificial synthetic biology uses man-made proteins to substitute fororganic DNA and proteins in the construction of novel life forms. Thedesign, testing and evolution of these artificial synthetic life formsare guided by metaheuristics. The AIS3 is a useful application toartificial synthetic biology because it organizes, reorganizes andoptimizes the artificial protein combinations to achieve a particularoutcome. By constantly testing the artificial organism with Layers 1 and2 of the AIS3, the system optimizes interactions with an evolvingenvironment. The artificial synthetic organism is modeled at Layer 3 andis able to learn by applying elements of the AIS3 multilayerinteractions.

Network Computing Applications

The AIS3 metaheuristic is applied to network computing applications.These applications include transformative databases, spatio-temporalobject relational distributed databases, enterprise systems, autonomiccomputing, network security, collective behavior of software agents,communication system optimization and distributed network scheduling androuting optimization.

(i) Transformative Databases

Transformative databases are active data storage structures thatperiodically reorganize their contents in order to optimize efficiency.Transformative databases are useful in network environments in whichthere are massive data sets and high performance requirements. Thedatabases actively analyze their data sets, categorize and re-categorizethe data and restructure the database periodically as a housekeepingfunction to maximize efficient throughput.

The AIS3 is useful in application to the transformative database inorder to actively anticipate data components in restructurationprocesses. Data sets from the database are analyzed by the AIS3 andspecific categories of data objects are reorganized. The process iscarried out by software agents that act autonomously in collectives.

(ii) Spatio-Temporal Object Relational (STOR) Distributed Databases

STOR databases are organized in a distributed network to coordinatefunctions in data collection and search. The AIS3 activates thereorganization process of the data sets in the distributed databases bysolving eMOOPs in real time. Before they store spatio-temporal datasets, the STOR databases are dynamic. The reorganization of data sets inthese dynamic databases produces a plasticity effect in the distributednetwork structure that is activated by the AIS3 as it solvesevolutionary combinatorial optimization problems.

(iii) Enterprise Systems

Enterprise systems are complex hardware and software configurations thatsupport the functions of businesses. Typically divided into functionalsubparts of human resources, accountings, manufacturing and so on,enterprise systems use databases to organize and connect data fromvarious business units. The AIS3 metaheuristic allows the software toautomate functions more readily. In fact, in some ways, enterprises areanalogous to organisms in an ecosystem (supply chain) in that they canbe viewed as a host where the AIS3 resides and in which it protects. TheAIS3 identifies and solves eMOOPs in enterprise systems by usingcollectives of software agents that self-organize.

(iv) Autonomic Computing

Designed to automate computer networks by emulating the autonomicfunctions of the human nervous system, autonomic computing is enhancedby the application of the AIS3. The self-regulating components of theautonomic computing system are optimized by the generation of solutionsto eMOOPs using the AIS3. When complex problems arise, the AIS3 modelinglayer simulates the problem and generates various solution options thatare applied in Layers 1 and 2. Once feedback is obtained on the initialresults of the solutions, the modeling layer further provides solutionoptions for implementation. Particularly because the autonomic computingenvironment is dynamic, it is well suited for the AIS3.

(v) Network Security

The classical application of the traditional AIS is to network security.The idea of emulating the HIS to defend against computer viruses is acompelling application of AISs. In particular, the network environmentfor the defense against computer viruses (malicious program code) is anappropriate application for the AIS3 as well. The present system goesfar beyond traditional AISs by providing sophisticated mechanisms forthe anticipation, acceleration and achievement of computer networksecurity goals within resource constraints.

(vi) Software Agent Collective Behavior

Self-organized software agent collectives present a type ofcomputational behavior to which the AIS3 is applicable. Software agentscooperate, collaborate and compete in order to perform specificfunctions automatically. The AIS3 is useful in facilitating the sociallearning mechanisms that are needed to carry out these processes.

Software agent collectives are applicable to transformative databases,autonomic computing and enterprise systems.

(vii) Communication System Optimization

Communication systems are improved by the use of the AIS3. Communicationsystems are optimized by the efficient use of network resources. At peaktimes in particular, the system requires continuous reorganization toefficiently maximize its resources. The AIS3 usefully optimizes theredistribution of resources in this communication system. As the load isrebalanced in communication networks, the system restructures usingplasticity effects. Specific nodes may be added or removed at differenttimes in order to minimize system burdens. The AIS3 metaheuristiccontinually optimizes this process.

(viii) Distributed Network Scheduling and Routing Optimization

The present system is useful in solving scheduling and routingoptimization problems in a distributed network. The AIS3 uses itsmultilayer modeling mechanism to actively solve eMOOPs involvingscheduling and routing. It continuously analyzes and solves multipleproblems simultaneously, with constantly updated solutions using thelatest information, yet within resource constraints. Application of theAIS3 to scheduling and routing optimization problems is a major advanceto the state of the art.

Evolutionary Systems

Evolutionary systems are classified into the categories of robotics,nanotechnology and programmable logic devices. Each of these representsa form of evolutionary hardware that may change its structure to performa function. The three main classes of evolvable hardware are the fieldprogrammable gate array (FPGA), the nanorobotics collective andcollective robotics.

(i) FPGAs

The FPGA is an integrated circuit with logic gates that reorganize itsstructure from one application specific integrated circuit (ASIC)position to another ASIC position. Since ASICs generally solve problemsfaster than microprocessors, FPGAs share benefits of ASICs (speed) andmicroprocessors (flexibility). FPGAs can be deterministic(pre-programmed functions) or indeterministic (continuouslyreprogrammable) or can possess limited evolutionary capability. FPGAscan be programmed to rapidly solve complex problems. They are useful intime-sensitive applications such as digital signal processing orembedded controllers.

The AIS3 is useful in assisting the FPGA in its programmable functionbecause the FPGA is interacting with an uncertain environment. Asproblems with the environment are encountered, the AIS3 Layers 1 and 2generate and test solutions. At Layer 3, new solutions to complexoptimization problems are analyzed and modeled and then tested at Layers1 and 2. The FPGA represents an ideal application of the AIS3 because itinteracts with its environment, receives feedback from the environment,restructures its configuration and continues in this feedback loop untilit performs its function.

In another embodiment, the system is also applied to networks ofasynchronous FPGAs. Much as it applies to network computing and toA-NNs, the AIS3 provides a mechanism to learn in an adaptivereconfigurable network environment which provides feedback. The AIS3uses its unique memory configuration in which it accesses globalinformation with local search processes to update its learning functionsso as to adapt to the environment. The FPGA network continuouslyrestructures until it satisfies its goals. In the context of acommunications network, this FPGA evolvable hardware network manifestscomplex plasticity effects and benefits.

(ii) Robotics

Hybrid robotic systems comprised of central and behavior-based controlsystems use the AIS3 model. These complex systems optimize the feedbackmechanisms from environmental inputs and the central control features ofrobotic manipulation. The AIS3 metaheuristic is useful in order for therobotic control systems to learn because optimization problems areconstantly evolving in the robotic environment as the robot navigatesits spatial domain to achieve its goals within resource constraints.

Robots are evolvable in some applications. For example, in the contextof manufacturing, robots will design and produce a unique part in realtime by employing fused deposition technology. The present system isuseful in order to help solve problems that facilitate this complexgoal.

(iii) Collective Robotics

CR also uses hybrid control systems for optimal functionality. As thevarious robotic units interact with their environment, they receivefeedback from their uncertain spatial domain. The distributed roboticnetwork coordinates actions between the units in the system much asantibodies coordinate behavior within their own collective. Thiscollective behavior is well organized by using the AIS3. Since the AIS3emulates the organization of antibody networks to solve complex eMOOPs,CR networks are an ideal application of this novel metaheuristic.Problems are solved within the first two layers in the ordinary courseof environmental interaction. However, the modeling layer isparticularly useful in order to accelerate the processes of the firsttwo layers. In addition, since CR systems are time sensitive becausethey interact with the environment in real time, the AIS3 is well suitedto solve CR eMOOPs in real time.

(iv) Collective Nanorobotics

The ability to produce electronics at increasingly smaller scales makespossible the development of nano- and micro-robotics. Nanorobots arereally only useful, however, if they are aggregated into collectives forspecific functionality. These nanorobotic collective applicationsinclude electronics functions and biological functions. In either case,the AIS3 is a useful metaheuristic to assist the nanorobotic collectivesin completing their goals in complex environments.

In the case of electronics, microrobotic collectives operate withincomputer devices to complete a specific function. Similarly, nano-scalerobotic collectives operate in electronics devices so as to optimizetheir mechatronic operations. In general, these are deterministicsystems.

Nanorobots are also applied to biological applications. In thisindeterministic application category, the nanorobotic collectives areused to emulate proteins in order to perform operations of dysfunctionalorganic proteins. The present system is useful in organizing thesebio-focused nanorobotic collectives. Because they are organized in adistributed network, the nanorobots use the mechanism of the AIS3 tolearn, adapt and reconfigure their collective behavior. In oneembodiment, the nanorobotic collectives use the AIS3 to fortify andoptimize the HIS so as to prevent disease. In order to perform thesefunctions, the nanorobotic collectives use collective behavior ofsoftware agents that also employ the AIS3.

Nanorobotic collectives, whether in electronic or biological systems,also engage in reorganization processes by using the AIS3. Thesereaggregation processes allow the nanorobots to create evolvablehardware configurations. The AIS3 metaheuristic facilitates the learningmechanisms that render the nanorobotic collective evolvable hardwarereaggregation processes useful, because it provides a way for the systemto reorganize in the context of environmental change.

Although the invention has been shown and described with respect to acertain embodiment or embodiments, it is obvious that equivalentalterations and modifications will occur to others skilled in the artupon the reading and understanding of this specification and the annexeddrawings. In particular regard to the various functions performed by theabove described elements (components, assemblies, devices, compositions,etc.) the terms (including a reference to a “means”) used to describesuch elements are intended to correspond, unless otherwise indicated, toany element that performs the specified function of the describedelement (i.e., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure that performs thefunction in the herein illustrated exemplary embodiment or embodimentsof the invention. In addition, while a particular feature of theinvention may have been described above with respect to only one or moreof several illustrated embodiments, such feature may be combined withone or more other features of the other embodiments, as may be desiredand advantageous for any given or particular application.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic drawing showing the hybrid multilayer artificialimmune system (AIS3) structure.

FIG. 2 is a flow chart describing the functional dynamics of the AIS3.

FIG. 3 is a flow chart showing how the adaptive immune system solves aproblem that is applied by the humoral immune system.

FIG. 4 is a schematic diagram illustrating the problem solving of adynamic problem by a metaheuristic over time.

FIG. 5 is a schematic diagram showing the AIS interacting with anenvironment which contains evolving antigens.

FIG. 6 is a chart showing the interaction of the three layers of theAIS3 to solve and model problems.

FIG. 7 is a schematic diagram showing the interaction of the AIS3 withantigens in an environment.

FIG. 8 is a set of three charts illustrating evolving multi-objectiveoptimization problems in the context of specifying a set of changingconstraints over three phases.

FIG. 9 is a schematic diagram showing a forward cascade process withartificial antibodies.

FIG. 10 is a schematic diagram showing a specialized stochasticdiffusion search (SDS) process for a forward cascade involvingartificial antibodies.

FIG. 11 is a set of three schematic diagrams showing the three phases ofa collection of antibodies interacting with an antigen in which theinitial solutions at layer one fail and the problem is transmitted tolayer two of the AIS3.

FIG. 12 is a schematic diagram showing the local connection between acollection of antibodies.

FIG. 13 is a schematic diagram showing the specialized neighborhoodconnection between a collection of antibodies.

FIG. 14 is a schematic diagram showing the clonal selection within theantibody generation process of a cascade of antibodies at layer 1.

FIG. 15 is a flow chart describing the process of antibody collectivecascade generation.

FIG. 16 is a flow chart showing the generation and application ofsolutions at layer 1.

FIG. 17 is a schematic diagram showing the interaction of specializedantibodies with the environment in which the antibodies obtain feedbackfrom, and experiment with, the environment.

FIG. 18 is a set of three schematic diagrams showing the evolution of anantigen and the multiple phases of interacting antibodies.

FIG. 19 is a flow chart describing the generation and experimentation ofantibodies to attack an antigen.

FIG. 20 is a set of three schematic diagrams showing the application ofdanger theory which differentiates self from dangerous objects such asantigens.

FIG. 21 is a flow chart showing the system generating solutions toantigen problems.

FIG. 22 is a flow chart showing the system solving different sorts ofantigen problems by distinguishing the quality of the antigens.

FIG. 23 is a schematic drawing showing the statistical model used toassess the relative danger of applying escalating solutions.

FIG. 24 is a flow chart describing the multiple variables and thresholdsrequired to attack an antigen with antibodies.

FIG. 25 is a schematic diagram showing a rapid antibody mutationevolution used to solve an antigen.

FIG. 26 is a flow chart showing the process of the combination ofantibodies used to destroy an antigen.

FIG. 27 is a schematic diagram showing the multiple solutions input intoa global memory which is accessed when a similar antigen is discovered.

FIG. 28 is a schematic diagram showing the interaction of the threelayers of the AIS3 with both the environment and the memory element.

FIG. 29 is a flow chart showing the process of passing the solutiongeneration to layer 2 to solve a problem of a new antigen.

FIG. 30 is a schematic diagram showing the process used by layer 2 tosolve an antigen problem, store the solution in memory and produce anantibiotic to solve a similar antigen problem at layer 1.

FIG. 31 is a flow chart showing the process of layer 2 generating asolution to solve an antigen and applying the solution at layer 1.

FIG. 32 is a schematic drawing showing the process of multipleinteracting antigens in an environment overwhelming one antigen.

FIG. 33 is a flow chart showing the process of generating, testing andadjusting a solution to an antigen.

FIG. 34 is a schematic diagram showing the process of collectiveteaching in which the information is transmitted globally for individualantibodies.

FIG. 35 is a schematic diagram showing the process of collectives ofantibodies teaching other antibodies and solving eMOOPs and entering thesolutions into a central database.

FIG. 36 is a schematic diagram showing the process of social teachingbetween antigens in which solutions that are produced at layer 2 arepushed to the next generation of antibodies used to apply the solutionat layer 1.

FIG. 37 is a schematic diagram showing the sharing of data withantibodies in a decentralized system.

FIG. 38 is a schematic diagram showing the three phases of fittingantibodies to a complementary mold of an evolving antigen.

FIG. 39 is a flow chart describing the process of an antibody generatingother antibodies to solve a problem of an antigen.

FIG. 40 is a schematic diagram showing the reverse engineering processof analyzing the evolution of an antigen.

FIG. 41 is a schematic diagram showing antigen mutation vector optionsin an environment aligned with antibody mutation vector options andstoring the solution results.

FIG. 42 is a schematic diagram showing the three layers of the AIS3 witha central memory component.

FIG. 43 is a schematic diagram illustrating the modeling process ofantigen mutation vectors.

FIG. 44 is a schematic diagram showing the interoperation of the threelayers of the AIS3.

FIG. 45 is a flow chart showing the interoperation of the three layers.

FIG. 46 is a schematic diagram that shows the layer 3 simulation optiongeneration process.

FIG. 47 is a flow chart showing the process of solving an antigenproblem by using the problem solving capabilities of layers 2 and 3.

FIG. 48 is a flow chart showing the process of using layer 3 to solve anoptimization problem.

FIG. 49 is a flow chart showing the process of layer 3 modeling anantigen ecosystem to develop scenario solution options.

FIG. 50 is a flow chart showing the process of layer 3 modeling anantigen ecosystem to predict antigen trajectories.

FIG. 51 is a schematic drawing showing several virus mutation vectorscenarios.

FIG. 52 is flow chart showing artificial virus hypermutation vectormodeling scenarios.

FIG. 53 is a schematic drawing showing a library of artificial virusesand the search for solutions to eMOOPs.

FIG. 54 is a schematic drawing showing a three dimensional snapshot ofan artificial virus.

FIG. 55 is a schematic drawing showing the connection between severalcollectives of antigens in an environment in which the system modelscompetition between the groups and cooperation within the groups.

FIG. 56 is a schematic drawing showing an antibody collective envelopingan evolving virus.

FIG. 57 is a schematic drawing showing the experimentation process ofadjusting variables and potentialities of antigens with several scenariosimulations.

FIG. 58 is a schematic drawing showing the reverse engineering processof the system to create an artificial synthetic vaccine with of virus toactivate AIS3 functions.

FIG. 59 is a schematic drawing showing the tagging of an antigen toattract an antibody collective.

FIG. 60 is a flow chart describing the process of using an artificialsynthetic vaccine to solve problems of an artificial virus.

FIG. 61 is a schematic drawing showing the timing of each layer'straining and the different thresholds of activation.

FIG. 62 is a schematic diagram showing the interactive process of thethree layers of the AIS integrated with the central database managementsystem.

FIG. 63 is a schematic diagram showing the use of distributed memory inthe three layer system.

FIG. 64 is a schematic diagram showing the process of accessing the mostrecent information from memory.

FIG. 65 is a flow chart showing how the system solves the problem of anevolving antigen at layers 1 and 2.

FIG. 66 is a schematic drawing showing how solution candidates aregenerated at layer 3, tested at layer 2 and applied at layer 1.

FIG. 67 is a schematic drawing showing the application of solutionsgenerated at layers 2 and 3 and the training process that buildsimmunity.

FIG. 68 is a schematic drawing showing the pre-set triggers at specificthresholds of the three layers that activate the cascade process atlayer 1.

FIG. 69 is a schematic drawing showing the process whereby layer 3develops a multi-scenario model with specification of conditions totrigger the cascade process of layer 1, including the possible variablesthat fit the profile of a hostile new antigen.

FIG. 70 is a flow chart describing the process of solving anoptimization problem using the three layers of the AIS3.

FIG. 71 is a schematic diagram showing the bucket brigade sequenceprocess of passing information between the layers of the AIS3 to solvean optimization problem.

FIG. 72 is a schematic diagram showing how layer 3 stimulates layer 2and how layer 3 stimulates layer 1 to perform specific functions.

FIG. 73 is a schematic diagram showing how data from layers 1 and 2 areinput to layer 3.

FIG. 74 is a schematic diagram showing how the three layers of the AISinteract with both distributed memory and with a central memory to storeand access data.

FIG. 75 is a flow chart showing the process of the interaction of thethree layers as they store and access data into databases in the AIS3.

FIG. 76 is a schematic diagram showing the parallel operations of layers1 and 2 and layers 2 and 3 to solve two separate problemssimultaneously.

FIG. 77 is a schematic diagram showing the parametric adaptation of theAIS3 in which the swelling size of the system configuration at phase tworeturns to equilibrium at phase three.

FIG. 78 is a schematic diagram showing three phases of co-evolutionaryplasticity at layer 3 in which growth and decline of activity returns toequilibrium.

FIG. 79 is a flow chart showing the process of using hybrid geneticalgorithms to solve problems in the AIS3.

FIG. 80 is a flow chart that shows the process of solving an eMOOPwithin time constraints using the three layers of the AIS3.

FIG. 81 is a schematic diagram showing the adaptation process byrestructuring of connection weights in an artificial neural network tosolve eMOOPs using the AIS3.

FIG. 82 is a schematic diagram showing the use of the AIS3 to trainartificial neural network connection weights.

FIG. 83 is a flow chart showing the process of training connectionweights in an artificial neural network using the AIS3.

FIG. 84 is a flow chart showing the process of solving a proteinregulatory network eMOOP using the AIS3.

FIG. 85 is a flow chart showing the process of solving a proteinstructure prediction eMOOP using the AIS3.

FIG. 86 is a flow chart showing the process of using the AIS3 to modeland adapt artificial protein combinations for synthetic biology.

FIG. 87 is a flow chart showing the process of using the AIS3 tooptimize storage pathways in a database management system.

FIG. 88 is a flow chart showing the process of solving an enterpriseresource allocation problem using the AIS3.

FIG. 89 is a flow chart showing the process of solving an autonomiccomputing problem using the AIS3.

FIG. 90 is a flow chart showing the process of solving a computernetwork virus problem using the AIS3.

FIG. 91 is a flow chart showing the process of reorganizing acommunications network to solve a distributed network problem by usingthe AIS3.

FIG. 92 is a flow chart showing the process of reorganizing thestructure of an FPGA using the AIS3.

FIG. 93 is a flow chart showing the process of solving a robotnavigation problem using the AIS3.

FIG. 94 is a flow chart showing the process of solving a collectiverobotics problem using the AIS3.

DETAILED DESCRIPTION OF THE DRAWINGS

The hybrid multi-layer artificial immune system (AIS3) operates in adistributed computing environment to solve evolutionary multi-objectiveoptimization problems (eMOOPs). The AIS3 functions by introducing aproblem into the system in the form of an antigen. The immunocomputingmodel analogizes the human immune system operation in which the humoralimmune subsystem discovers antigens and responds by accessing memory andby introducing multiple levels of antibodies to attack the antigens. Thememory system is informed by the adaptive immune subsystem which solvesproblems involving novel antigens and passes these solutions to thehumoral subsystem in the form of immunity. The present system introducesthe concept of the anticipatory modeling layer and the dynamics of themodeling layer with other layers in the integrated system. The use ofmemory to store and access new solutions, combined with the modelingcomponent, presents an advanced immunocomputing system withfundamentally novel metaheuristics approaches for solving complexoptimization problems.

The AIS3 structure is illustrated in FIG. 1. The computer system (170)is connected to layer 1 (155), layer 2 (165), layer 3 (180) and memory(175). Layers 1 and 2 are connected to a database management system(160) for data storage and access. The modeling system interacts withlayer 2 in this model. Layers 1 and 2 produce antibodies (125-152)contained within the environment (120) wherein the end result is thesolution (152). The antibodies interact with and track the antigens (A,B and C) (105, 110 and 115) as they co-evolve in their environment(100). The solution (at 152) corresponds to the last antigen phase(117).

The three layer functionality of the AIS3 is described in FIG. 2. In thehumoral immune subsystem of layer 1, the system identifies a previouslyencountered antigen (200) and activates the cascade process to produceantibodies to defeat the antigen (i.e., solve the optimization problem)by accessing the dbms (210 and 295) and ultimately solves the problem(220). In the adaptive immune subsystem of layer 2, the systemidentifies a new antigen (230) and solves the MOOP by using anexperimental process (240). The solution is then stored in the dbms(250) for later access by layer 1. At layer 3, the system modelssolutions to MOOPs (260), anticipates MOOPs (270) and solves MOOPs(280), which solutions are then stored in the dbms (290) for lateraccess by layer 1.

FIG. 3 shows the process of solving a problem at layer 2 and applyingthe solution at layer 1. Once a problem is identified (300), theadaptive immune system solves the problem (310) and updates memory withthe solution (320). The humoral immune system accesses the memory (330)and applies the solution (340).

The AIS3 metaheuristic is particularly well suited to solvingevolutionary optimization problems because it is adaptive. FIG. 4 showsthe problem solving process of a dynamic problem over time. While theproblem emerges (420), the frontiers of change (410) evolve as theenvironment changes (400). The system continually seeks to solveevolutionary problems (430), makes multiple solution attempts andmatches the solution to the problem (440).

The AIS3 solves problems of antigens in an environmental context. FIG. 5shows the environment (510) in which the antigens (520) interoperate.Both the environment and the interacting AIS3 (500) evolve in time.

The chart in FIG. 6 shows the interaction of the three layers withinformation, problem solving and modeling aspects. Layer 1 providesinformation to both layers 2 and 3. Layer 1 also provides information tolayer 3 for problem solving, while layers 2 and 3 provide problemsolving analyses to each other. Layer 3 provides modeling analyses toboth layers 2 and 3 for application to solve a MOOP.

FIG. 7 shows the interaction of antigens with the AIS3. The antigens(720) are in an environment (710) and interact with the AIS3 (705) in ahost (700) computer system.

FIG. 8 shows three phases of an eMOOP with changing constraints overtime. The three parallel lines in phases A, B and C (802, 804 and 806,820, 822 and 824, and 840, 842 and 844) and the intersecting line (810,830 and 848) remain constant while the intersecting lines (816, 836 and854 and 812, 832 and 850) and the parabolic curves (808, 826 and 846 and814, 834 and 852) change positions in different phases of the process.These lines and curves indicate constraint categories with frontiers ofchange, illustrating evolutionary dynamics of multi-attributeoptimization problems over time. Each antigen evolves over time andexhibits multiple attributes that are described by eMOOPs and solved bythe AIS3. In other embodiments, the constraint categories are modeled in3D and 4D dynamics. Priorities between constraints are constantlyadjusted over time.

FIGS. 9 and 10 show the forward cascade process with artificialantibodies. In FIG. 9, the antibodies (900, 905 and 910) at the leadingedge of the group transmit information forward in their local area. 900transmits information to 915, 925 and 930. 905 transmits information to915, 920, 930 and 940. 910 transmits information to 920, 925, 930, 935and 940. FIG. 10 shows the use of the stochastic diffusion search (SDS)metaheuristic for a forward cascade involving artificial antibodies. Theantibody types (A, B, X and Y) at the leading edge (1000, 1020, 1040 and1060) transmit information forward to antibodies of the same type (A toA, B to B, etc.). The ability for antibody types to transmit informationto specialized antibodies allows for the systematic performance of thedivision of labor for maximum efficiency.

In FIG. 11, the three phases of an antibody collective are showninteracting with an antigen. At phase one, the initial solution attemptby the antibodies (1100) against the antigen (1105) fail. The systemthan transmits the problem to layer 2. At phase two of the figure, theantibodies (1115 to 1160) are shown to be classified into differenttypes (A, B, X and Y) that work together to solve the problem (1110).Once the problem is solved at phase two, the antibodies (1170) aredissolved at phase three.

The antibodies interact with each other by local networking connections.FIG. 12 shows the local network contacts between antigens. Each antibodyis connected to its nearest neighbors.

The specialized network interactions of the neighborhood connections aredescribed in FIG. 13. The “A” antibodies are connected to each other(1300, 1305 and 1310), the “B” antibodies are connected to each other(1315, 1320 and 1325) and the “C” antibodies are connected in a diamondconfiguration. The local network connection, the specialized antibodiesand the SDS metaheuristic are combined to produce clonal antibodies asillustrated in FIG. 14. In this model, the specialized antibodies aregenerated in consecutive order and communicate directly with otherspecialized antibodies of the same type. The X antibody (1400) producesthe X antibodies in order at 1405, 1410 and 1420. The Y antibody (1425)produces the Y antibodies at 1430, 1435 and 1440. Finally, the Zantibody (1445) produces the X antibodies at 1450, 1455 and 1460.

The antibody collective cascade generation process is described in theflow chart in FIG. 15. The system initially generates antibodies in thecategories X, Y and Z (1500). The system then encounters antigens (1510)and the antibodies X, Y and Z generate clones in each category in thenext phase (1520). The clonal antibodies in groups X, Y and Z interactwith antigens (1530) and the system generates new X, Y and Z antibodyclones in a second phase (1540). The system achieves a critical mass ofantibodies to solve the problem of the antigens (1550).

FIG. 16 shows the generation and application of a solution at layer 1.After the system is in equilibrium (1600), the system assesses theproblem (1610), generates a solution (1620) by accessing a database ofknown solutions to known problems and applies a solution (1630). Thesystem then returns to equilibrium.

FIG. 17 shows the interaction of specialized antibodies with theenvironment in which the antibodies obtain feedback from, and experimentwith, the environment. Antibody X (1710) initially interacts with theenvironment (1700) and moves to position 1720. It interacts again withthe environment and then moves to position 1730. Antibody Y moves fromposition 1740, interacts with the environment and moves to positions1750 and 1760 in sequential order while interacting with theenvironment. Antibody Z experiences the same interactive sequence withthe environment (1770-1790).

FIG. 18 shows the interaction between the antibodies and the environmentover three phases. In the first phase, antibodies X, Y and Z (1802, 1804and 1806) proceed to access the environment, while the X antibodies(1808-1824) receive feedback from the antigen at phase A. At phase two,the antibodies X, Y and Z receive feedback of antibodies Y (1828-1832)to the antigen at phase B 1820). Finally, at phase three, the antibodiesX, Y and Z receive feedback of antibodies Z (1848-1854) to the antigenat phase C (1840).

FIG. 19 shows the process of generating and experimenting withantibodies to attack an antigen. After the system identifies antigen R(1900), the system accesses the database of known antigens to categorizeantigen R (1910). The system activates layer 1 antibodies X, Y and Z toattack the antigen R (1920) and obtains feedback to assess the effect ofthe antibodies on antigen R (1930). Either the supplemental solutionworks and the antigen is defeated (1940) or the solution fails and thesystem proceeds to experiment with existing antibodies to defeat antigenR (1950). If the solution works, the system returns to equilibrium(1970). If the solution fails, the system proceeds to layer 2 to solvethe problem (1960).

The AIS3 uses danger theory to distinguish itself from foreign objectssuch as antigens. FIG. 20 shows three phases in which the AIS3 usesdanger theory to differentiate itself from dangerous objects. In phaseI, the antibodies, shown here in two types (2002 and 2005), at layer 1are separated in the system from the antigen (2000), which lies outsidethe system. However, in phase II, the antibodies encounter the antigenwhich moves from position 2010 to position 2027 to position 2030. Theantibodies interact with each other using local search. The antibodiesdistinguish each main antibody type and categorize the other antibodiesas antibodies. While realizing the similarities of the antibodies, theantibodies distinguish between the foreign antigen. At phase III, theantibodies clearly identify, target and attack the antigen (2040) as ahostile invader. By using the principles of identity and difference, theAIS3 clearly identifies that antigens are foreign to the system andproceeds to attack them.

In another embodiment of the system, the antigen identification processis accelerated by implementing the anticipation mechanisms of layer 3.The identification of antigens then occurs as the system anticipatesspecific antigen types entering the system in particular patterns forincreasingly rapid identification threshold activation. Preliminaryconditions for antigen generation modeled at layer 3, such as a changein an antigen's environment, will trigger the activation threshold.

FIG. 21 shows how the system generates solutions to antigen problems.After antigens are injected into the system (2100), the quantity ofantigens achieves a critical mass (2110) and the system detects theantigens (2120). The system then assesses the configuration of theantigen (2130), accesses a database and compares the antigen topreviously identified antigens (2140). The system identifies an antigenin the database (2150) and activates antibodies to attack the antigen(2160). This process solves the problem (2170) and the system returns toequilibrium.

The system is also designed to distinguish between multiple types ofantigens. FIG. 22 describes the process of solving different problemtypes by distinguishing between the quality of the antigens. After thesystem assesses antigens A and B (2200), antigen A is detected as a mildantigen (2210) and antigen B is detected as an aggressive antigen(2220). The system detects the type and quality of the antigens byaccessing a database and comparing antigens A and B to past antigens(2230) in specific categories and according to particular properties.The system attacks antigen A with a minor response (2240) and escalatesa response to attack antigen B with a higher number of antibodies(2250). This model optimizes the efficiency of computation resources.

As a result of these actions, the system solves both types of problems.The escalation process of attacking a relatively more aggressive antigenwith more aggressive responses involves the need to distinguish betweenqualities of antigens. In one respect, a new antigen which is not yetclearly classified as benign or aggressive will require the system toescalate the attack on the antigen over time, by efficiently preservingscarce resources initially and severely attacking an aggressive antigenas it receives feedback from the system of resisting the escalation ofresources.

In FIG. 23, a statistical model is used to assess the relative danger ofapplying escalating solutions. In the left column, antigen A (2300, 2305and 2310) is represented as moving downward over time. As it interactswith the antibodies in the middle column, it is initially viewed asrequiring twenty percent of available resources to defeat. On the otherhand, in the right column, antigen B (2335, 2340 and 2345) isrepresented as moving downward over time. However, this more aggressiveantigen is initially identified as being much more hostile and requireseighty percent of the system's available resources to defeat. At eachphase, the system at layer 1 brings more antibodies to attack theaggressive antigen. To do this, the system escalates the generation ofantibody clones (2360, 2365 and 2370 at phase two and 2375, 2380, 2385and 2390 at phase three) to generate sufficient capacity to defeat theantigen.

FIG. 24 shows the process whereby multiple variables and thresholds arerequired to attack an antigen with antibodies. After the system assessesan antigen (2400), it analyzes external conditions for the antigen(2410) and evaluates the multiple variables of the external environmentto assess the antigen's optimal conditions (2420). The system identifiesthreshold(s) to attack the antigen by generating specific antibodies(2430) and receives feedback on initial antibody interaction with theantigen (2440). The system then adjusts the threshold of antigen optimalconditions (2450) and generates antibodies to attack the antigen (2460).This process solves the problem.

In FIG. 25, the antibody evolution process is shown that requiresmutation. In an external environment (2500), the antigens evolve fromposition 2510 to positions 2520, 2530 and 2540. As the antibodies on theleft side of the diagram interact with the antigen, they co-evolveaccording to specific mutation vectors. After the initial antibodydevelopment phase (2550), the third arrow depicts the direction ofevolution for the second phase (2560), the first arrow of the next phasedepicts the direction of evolution for the third phase (2570) and thesecond arrow of the last phase depicts the direction of the evolution ofthe last phase (2580). At each phase, interactions with the antigenguide the direction of evolution for the antibodies in order for theantibodies to successfully defeat the antigen at 2450.

FIG. 26 describes the process of antibody combination used to destroy anantigen. Once the system identifies an antigen (2600), the antigenrapidly mutates its structure (2610). The system then assesses theantigen mutation configuration vectors (2620) and creates antibodies tosolve the antigen problem (2630). The system combines antibodies to fitthe configuration of the evolving antigen (2640) and the system adjuststhe rate and degree of antibody mutation to match the antigen evolutionrate (2650). The system destroys the antigen (2660), solves the problem(2670) and returns to equilibrium.

FIG. 27 shows how multiple solutions are input into a global memory,which is later accessed when a similar antigen is discovered. Theantigens (2710 to 2720 and 2735 to 2750) interact with antibodies (2705,2715 and 2725 and 2730, 2740 and 2745) and the antibodies solve theproblem of the antigens. Once solved, the solutions are entered into thecentral database (2700). In later encounters with the same antigens(2755 to 2765 and 2770 to 2780), the system accesses the database,discovers the antigen categories and the appropriate solutions andpasses this information to the antibodies (2760 and 2775) which are ableto rapidly apply the same solutions and eradicate the antigens. Thisprocess maximizes efficiency and preserves computational resources.

FIG. 28 shows the interaction of the three layers of the AIS3 with boththe environment and the memory element. The environment (2800) hostsantigens (2810) which interact with layers 1 (2820) and 2 (2830). Layer2 solves novel problems and inputs data into memory (2850) while layer 1accesses the information in memory. Layer 3 (2840) interacts with layer2 in order to provide analytical capacity to assist in solving noveloptimization problems.

FIG. 29 shows the process of passing the solution generation from layer1 to layer 2 to solve a problem of a new antigen. After the systemencounters an antigen (2900), it uses layer 1 to compare the antigen tothe antigens in the database (2910). The system does not identify thenew antigen in the database (2920) and passes the problem to layer 2.Layer 2 creates antibodies to solve the problem of the new antigen(2930) and the solution is stored in database memory (2940). The sameantigen is encountered at a later time (2950) and layer 1 accesses thememory in the database to obtain a solution (2960) after which layer 1solves the problem (2970).

FIG. 30 shows the process used by layer 2 to solve an antigen problem,store the solution in memory and produce an antibiotic to solve asimilar antigen problem at layer 1. The antigen (3000) is encountered bythe antibody (3030), which tracks the antigen and solves it at 3050 andstores the solution (3060). Once the same antigen (3065) is discoveredlater, the antibody (3070) accesses the database and, at layer 1, afterdiscerning the same antigen, acquires the solution to handily solves theantigen problem at 3075.

FIG. 31 is a flow chart that shows the process of generating a solutionat layer 2 to solve an antigen and applying the solution at layer 1.Once the system identifies a new antigen X (3100), layer 2 producesantibodies to attack antigen X (3110) and the solution is entered into acentral database (3120). Antigen X-2 is then discovered (3130) and thesystem accesses a database to acquire the antibody combination solutionto antigen X (3140). The system then applies an antibiotic consisting ofthe antibody combination solution used to solve antigen X (3150) andantigen X-2 (a derivative of antigen X) is solved at layer 1 (3160). Thenew solution is stored in the database.

Antigens exist in an environment in which they are interactive with eachother. In FIG. 32, multiple interacting antigens are shown in anenvironment overwhelming an evolving antigen. Within the environment(3200), the antigen moving downward in the left column (3202-3212) andthe antigen moving downward in the right column (3224-3236) interactwith the antigen in the center column (3214-3222) in multiple phases.Because of this interaction, the center antigen is diminished. Theantibodies on the far left column (3240-3246) interact with the antigensin the environment until the center antigen is solved at 3222.

FIG. 33 shows the process of generating, testing and adjusting asolution to an antigen. After the system identifies the antigen Y(3300), layer 2 compares antigen Y to similar antigens in the database(3305) and identifies prior solutions to antigens similar to antigen Y(3310). Layer 2 then assesses the main variables of antigen Y bydistinguishing between variables from similar antigens in the database(3315). Layer 2 assesses the vectors of antigen Y evolution (3320) andgenerates solution candidates to solve evolving antigen Y (3325). Layer2 tests each solution option to each phase of antigen Y (3330)development and receives feedback to application of solution options toantigen Y (3335). Layer 2 ranks each solution option (3340) and adjustssolution options to solve evolving antigen Y (3345). The new solution isstored in the database (3350).

FIG. 34 shows the process of collective teaching in which theinformation is transmitted globally for individual antibodies. AntibodyA, which evolves at positions A1 (3400) and A2 (3405), interacts withantibodies at 3410 to 3420. Similarly, antibody B, which evolves atposition B1 (3425) and B2 (3430), interacts with antibodies at 3435 to3445. The solutions to the problems are then entered into a centraldatabase (3450). When future antigens are identified (3452, 3462 and3472), antibodies are generated (3456, 3466 and 3476) to solve the newlyencountered antigens by accessing the database for prior solutions. Thelatest solutions (at 3460, 3470 and 3480) are then entered into thedatabase for future access. This model allows the storage and access ofmultiple combinatorial optimization solutions simultaneously. In thismulti-phasal process, the prior antibodies teach future antibodies tosolve problems.

FIG. 35 shows the process of collectives of antibodies teaching otherantibodies and solving eMOOPs. As they evolve, several groups ofantibodies (3505-3525, 3530-3545 and 3555-3570) teach other antibodiesdirectly about their experiences and solve the emerging antigens (3520,3550 and 3580, respectively). The solutions to the newly encounteredantigens are applied immediately without access to a database becausethe antibodies share information directly. However, the solutions mayalso be generated by accessing a database once the antigens are detectedand identified at layer 1; thus, past solutions are passed to newproblems on-demand, illustrating multiple learning components of thesystem. The solutions may be generated to solve the antigens at anyphase of evolution. Once the antigens are solved, the new solutions areentered into the database (3500).

FIG. 36 shows the process of social teaching between antigens in whichsolutions that are produced at layer 2 are pushed to the next generationof antibodies used to apply the solution at layer 1. As the antibodiesevolve (3600-3610 and 3625-3635) at layer 2, they encounter and solveantigen problems (3615-3620 and 3640-3645) and enter the solutions (3610and 3635) into the database (3650). At layer 1, antibodies (3655 and3670) then interact with the newly encountered antigens (3665 and 3680)and solve the problems rapidly (3660 and 3675) by accessing the databasefor past solutions to known antigens. The teaching process occurs bylayer 2 pushing the solutions to new antibodies at layer 1 to solve anew generation of problems.

FIG. 37 shows the sharing of data with antibodies in a decentralizedsystem. In this figure, a set of antibodies (3700-3710) interacts withan evolving antigen (3715-3720) and solves the problem at 3710. Thissolution is then passed on directly to a set of antibodies (3725), whichinteracts with another evolving antigen (3740-3745) and solves theproblem at 3735. Similarly, this solution is passed directly to theantibodies at 3750. As these antibodies (3750-3760) interact with theevolving antigen (3765-3770), the antigen is solved at 3770. Thisprocess is significant because it passes solutions directly tosuccessive antibodies.

Antibodies solve optimization problems by satisfying the constraints ofthe multi-objective problem (antigen). In FIG. 38, the model is shown ofthe process of fitting antibodies to a complementary mold of an evolvingantigen. At the first phase of the process, the antigen (3800) and theantibodies (3810 and 3820) are identified. At the second phase, theantigen (3830) is surrounded by cloned antibodies (3840) which proceedto form around the antigen. Finally, at the third phase, the antigen(3850) is fully surrounded by the antibodies (3860), which solve theproblem. In the HIS, antibodies solve the problem encountered by anantigen by identifying the specific geometric contours of the antigenand by enveloping the antigen. The present model emulates some elementsof this approach by analogy by deciphering the components of the antigenand by solving the constraints of the eMOOP.

In FIG. 39, the process of an antibody that generates other antibodiesto solve an optimization problem is shown. Once the system encountersantigen A (3900), antibody R encounters antigen A and accesses a centraldatabase (3910). However, antigen A is not discovered in the database asa novel antigen (3920) and antibody R recruits antibodies to surroundantigen A (3930). A collective of antibodies surround antigen A tocreate a complementary mold (3940). Data on the antigen A configurationis transmitted to the central database by the antibody collective (3950)and the antibody collective creates a complementary mold of antigen A(3960). The antibody collective generates a replica of antigen A andconducts analysis (3970) and solves the MOOP generated by antigen A(3980).

FIG. 40 shows the reverse engineering process of analyzing the evolutionof an antigen. Once evolutionary antigens (4000-4015 and 4050-4060) aredetected by antibodies (4020 and 4065), the antibodies immediatelyaccess the central database (4045) to obtain solutions to the antigens.The antibodies then analyze the antigens (4030 and 4072), developsolutions at several phases and enter the data into the database. Again,the antibodies access the database (4035 and 4075) in order to obtaindata to use in the analyses to solve the eMOOPs. Once the eMOOPs aresolved, the solutions (4040 and 4077) are entered into the database. Ata later phase, antibodies (4080-4090) interact with newly encounteredantigens (4095-4097) and solve the new antigens at 4090 by accessingprior database solutions.

FIG. 41 shows antigen mutation vector options in an environment alignedwith antibody mutation vector options and storing the solution results.The evolving antigen (4105-4125) within the environment (4100)demonstrates the multiple phases of the antigen mutation vectors. To theright side, the antibodies (4135-4160) evolve in a parallel track tomirror the antigen evolution. The antibody solution (4160) to the finalphase of the antigen problem (4125) is stored in the central database(4130).

FIG. 42 shows the three layers of the AIS3 with a central memorycomponent. In this figure, layer 1 (4200) provides information to layer2 (4210), stores information into and accesses the central memory (4230)and receives modeling data from layer 3 (4240). Layer 2 (4210) providesinformation to, and receives information from, layer 3 and stores datain the central database. Layer 3 stores and accesses information in thedatabase and provides information to both layers 1 and 2.

In FIG. 43, the modeling process of antigen mutation vectors is shown.From the first phase, in generation 1, the antigen (4310) evolves on aspecific vector to a position at generation 2 (at 4320). At generation3, the antigen evolves to a new position on a vector at 4330, while atgeneration 4, the antigen evolves to a new position on a vector at 4340and at generation 5, the antigen evolves to a new position on a vectorat 4350. Modeling the evolutionary characteristics of the antigen iscritical to understanding and solving the problem.

FIG. 44 shows the interaction of the three layers of the AIS. Layer 1(4400) encounters an antigen (4450) and generates solution options(4410) by accessing memory (4475). If it cannot produce successfulsolutions to solve the problem of the antigen, then the problem isforwarded to layer 2. At layer 2, the antigen is accessed as it evolvesfrom 4455 to 4460. “N” (i.e., an indeterminate) number of solutionattempts are constructed between 4420 and 4425 to generate solutionoptions which are tested at 4460. If the solution is developed at layertwo, the solution is stored in memory. If the solution is not generatedat layer 2, the problem is forwarded to layer 3, where the problem(4465) is modeled at 4435. The modeling process will engage “N” numberof modeling simulations between 4435 and 4440 to seek solutions and thenenter the solution at 4440 in memory.

FIG. 45 further shows the interoperation of the three layers. After thesolution candidate is generated at layer 1 (4500), the question is askedwhether the solution candidate solves the problem (4510). If it doessolve the problem, then the solution is stored in memory (4520) and thesystem achieves equilibrium (4530). If it does not solve the problem,then the system proceeds to layer 2. After solution candidates aregenerated (4540) at layer 2, the question is again asked if the solutioncandidate solves the problem (4550). If it does solve the problem, thenthe solution is stored in memory and the system achieves equilibrium. Ifit does not solve the problem, the system proceeds to model the problemat layer 3 (4560). Layer 3 produces solution candidates (4570), teststhe solution candidates (4580) and repeats this process until it solvesthe problem. Once the problem is solved, the solution is stored inmemory and the system achieves equilibrium.

FIG. 46 shows the layer 3 simulation option generation process. In thisdrawing, the evolving antigen (4625-4640) is represented in anenvironment. Two parallel simulations of antibodies are constructed(4600-4615 and 4645-4660) with evolving mutation vectors in order toreach solution candidates (4615 and 4660). The solution candidates arethen tested (4620). In fact, though there are two simulations referencedin this drawing, the system uses a multitude of parallel simultaneousmodeling approaches to model and solve the antigens.

FIG. 47 shows the process of solving an antigen problem by using theproblem solving capabilities of layers 2 and 3. Once the systemencounters an antigen (4700), it generates simulations of scenarios ofantigen eMOOPs at layer 3 (4710). Layer 3 generates simulations ofantibody collective behaviors to present solutions candidates (4720).The modeling simulations of antigens and antibodies train layer 2solution generation (4730). By using the simulations, and statisticalmodeling processes that anticipate antigen evolution trajectories(4740), layer 2 accesses layer 3 modeling and adapts to the evolvingantigen mutation pathways (4760). Layer 2 then solves the eMOOP andstores the solution in the database (4740).

FIG. 48 shows the process of layer 3 modeling an antigen ecosystem todevelop scenario solution options. After layer 3 generates models ofpotential synthetic antigen R (4800), it forecasts multiple scenarios ofsynthetic antigen R (4810). Layer 3 then generates a simulation ofantibodies to solve synthetic antigen R (4820). The system encountersantigen R (4830) and the layer 3 model of antibodies solves the problemof antigen R and stores the solution in a database (4840). When thesystem once again encounters antigen R (4850), the system pro-activelyseeks out and attacks antigen R in real time at layer 1 (4860) byaccessing the memory in the database. The antigen R MOOP is solved(4870).

FIG. 49 shows the process of layer 3 modeling an antigen ecosystem todevelop scenario solution options for an eMOOP. Layer 3 initiates amodel to simulate antigen ecosystem (4900), which model simulationsinclude analysis of environmental variables in multiple variables(4910). The layer 3 antigen ecosystem model then presents simulations ofantigen environmental condition variables (4920) and layer 3 ecosystemmodel simulates the antigen's equilibrium conditions (4930). Layer 3models relations between multiple antigen interactions by developingmultiple scenarios (4940) of behaviors and layer 3 develops multiplemodels of scenarios of synthetic antigen ecosystem networks (4950). Themulti-antigen modeling is accessed by layer 2 to develop solutions toeMOOPs (4960) in real time as new antigens are encountered. The systemsolves the antigen problem and stores the solution in a database (4970).

FIG. 50 shows the process of layer 3 modeling an antigen ecosystem topredict antigen trajectories. After layer 3 tracks antigen evolutionvectors in an ecosystem (5000), a rapid change in the antigen ecosystemequilibrium occurs (5010) and layer 3 detects the change (5020). Layer 3models a spike in antigen ecosystem conditions (5030) and analyzespatterns in the antigen exogenous environment (5040). The layer 3 modelof the antigen ecosystem then predicts trajectories of specific antigens(5050) and the layer 3 modeling and anticipations of the antigenevolution trajectories are accessed by layer 2 to solve eMOOPs (5060).Layer 3 possible solutions are matched to actual antigens at layer 1(5070) and the problems are solved and stored.

FIGS. 51 to 60 describe the system's analysis and response to viruses, aparticular class of antigens. FIG. 51 shows the evolution scenarios of avirus. At 5100, the virus may evolve into either 5110 or 5140 dependingon its mutation vectors and environmental conditions. From 5110, it willevolve into phases at 5120 and 5130. In this case, its evolutionarydevelopment is limited. For instance, in this first scenario, itsevolutionary track may be limited by restrictive environmentalconditions. At 5140, however, the virus will develop either to position5150 or 5170. If it evolves to position 5150, the positive environmentalconditions will allow it to flourish at a later phase at 5160. If itevolves to the mutation vector at 5170, its development will remainrestricted at the next phase at position 5180.

FIG. 52 shows the artificial virus hypermutation vector modelingscenarios. After the system models the artificial virus hypermutationdirection vectors (5200), the vectors are determined to be either slow(5210) (in a cold environment), normal (5230) (in an averageenvironment) or rapid (5250) (in a hot environment). If the virus'svector is slow, its development is slow (5220). If its vector is normal,its development is normal (5240). Finally, if its vector is rapid, itsdevelopment is accelerated (5260).

FIG. 53 illustrates a library of artificial viruses and the search forsolutions to eMOOPs. On the left, the library (5300) or catalogue ofartificial viruses organizes the viruses (5310) by category, phase ofdevelopment and environmental condition. This organization system isanalogized a periodic table for chemicals in which the variables ofchemicals are organized into specific categories. These virus attributesfit into a common classification scheme. On the right side is a table(5320) which describes the corresponding search for solutions (5330) tothe specific viruses that are classified in the virus library.

In FIG. 54, a three dimensional (5410) snapshot of an artificial virusis shown. It is important to realize that while most representations ofviruses are two dimensional, the artificial synthetic virus isanalogized to a real virus with three dimensions of geometric extensionin space. In addition, the analysis of the evolutionary process of theartificial virus maps the virus over time. Therefore, a four dimensionalanimation of virus evolution is critical to understanding the AIS3modeling process for analysis of artificial and synthetic viruses. With4D animations, the system is able to analyze the virus mutation vectorsat multiple phases, is able to compare the virus evolution with otherviruses and is able to predict and anticipate the probable evolutionarypathways of complex eMOOPs. Only by accurately analyzing eMOOPs cansolutions be developed in a timely way to satisfy critical constraints.

FIG. 55 shows the connection between several collectives of antigens inan environment in which the system models competition between the groupsand cooperation within groups. In the social settings among antigens inan artificial environment (5500), there is both cooperation andcompetition. Within specific antigen family types (5505, 5525 and 5560),there is cooperation between members which share common attributes.However, between the families of antigens there is competition.

FIG. 56 shows an antibody collective enveloping an evolving virus. Thevirus (5600) at the first phase is encountered by the antibodies (5610and 5625) which recruit or clone other antibodies (5605, 5615 and 5620).The antibody group (5635) at phase two approaches the virus. At phasethree, the antibodies (5645) collectively surround the virus (5640). Atthe final phase, the virus (5650) is enveloped by the antibodies (5660)and the problem is solved. This process shows the four dimensionalcharacter of identification and destruction of a virus by an antibodycollective.

FIG. 57 shows the experimentation process of adjusting variables andpotentialities of antigens with several scenario simulations. Theantigen at 5700 evolves into several vector pathway potentialities(5710, 5740 and 5770); each of these evolutionary potentialitiesdevelops its own evolutionary pathway based on its distinctivevariables. The AIS3 layer 3 models the simulations of each of theantigen evolutionary pathways by experimenting with the potentialities.The evolutionary development process of 5710 moves to 5720 and 5730,while 5740 moves to 5750 and 5760 and 5770 moves to 5780 and 5790. Thedotted lines reflect potentialities of the simulated development of theantigen simulations.

FIG. 58 shows the reverse engineering process of the system to create anartificial synthetic vaccine of a virus to activate AIS3 functions. Avirus (5800) is modeled to produce an analysis of its components (5810).These components are disassociated (5815-5860) in the third phase of theprocess. The separate parts of the virus are used to seed an artificialvaccine to customize a solution to the particular virus. The vaccine isused to inoculate the system at layer 1 by anticipating a known antigen,which, when encountered, is rapidly solved by accelerating the system'sproblem solving functions.

FIG. 59 shows the tagging of an antigen to attract an antibodycollective. When an antigen (5900) is discovered by an antibody (5950),the antigen is tagged (5905) by an antibody (5955) and the taggedantigen attracts a collective of antibodies (5960-5990). The tagging ofthe antigen allows the system to track the antigen's evolutionarypathways. Antibodies are attracted to the tagged antigen (5910) at thenext phase (5915 and 5920). Layer 3 analyzes the antigen (5930) andgenerates modeling scenarios (5935) using the tagged antigen and theantibody collective information. The eMOOPs solve (5940) the antigenproblem (5925) and store the solution in memory (5945).

In FIG. 60, the process of using an artificial synthetic vaccine tosolve problems of an artificial virus is described. After the systemidentifies a new virus (6000), layer 1 passes the problem to layer 2(6010) and layer 3 models the new virus (6020). The new virus isdefeated by applying a new solution at layer 1 (6030). Layer 3 reverseengineers parts of a new virus to create a vaccine (6040) and the systemstores the solution and vaccine in memory (6050). The artificialsynthetic vaccine of the new virus is input into layer 1 to guide asolution (6060) to the virus and when the system rapidly detects a newvirus, it activates layer 1 (6070). Layer 1 accesses a database, rapidlyacquires a solution (6080) and applies the solution to solve the problem(6090).

FIG. 61 shows the timing of each layer's training and the differentthresholds of activation. In the first phase, the antigen (6105-6115) isencountered and assessed by layer 1 (6100). Once this process ofanalysis is performed by accessing the memory (6150) to compare theantigen to past problems and solutions, the decision is made to pass theproblem to the second layer (6125 to 6130). The threshold of activationof layer 2 (6120) is set by the achievement of criteria in layer 1.Analysis of particularly complex antigens is also passed to layer 3(6135) once their complexity satisfies a specific threshold (6140) tojustify use of these modeling resources. Once the problem is solved(6145), the solution is entered into memory (6150). The escalation offunctions efficiently conserves computational resources.

The central database model is important to immunocomputing systemsbecause of the utility of passing information, particularly solutions toproblems, from one layer to another, for rapid solution generation tolater problems. Another main model used to show the use of memorystorage is to implement the system by using a distributed databasemanagement system in a computer system.

The dbms may be either a central database or a distributed database. TheAIS3 may use either model. In the case of the central database, themultiple layers of the AIS3 stores data from all layers to be used byall layers. In the case of the distributed database, however, eachsystem layer uses its own memory. This approach has the advantage ofrapid storage and access in a distributed network in which each layer islocated in a separate domain.

A third model synthesizes the two memory models by combining adistributed memory approach with a central memory approach. In thiscase, while each layer has its own memory capability, the three layersshare a central memory as well for inter-layer access; this modelproduces redundancies that back up data from problem solving functionsin real time in both the distributed and central databases. Since thememory system is critical to the AIS3, and to metaheuristic systems ingeneral because they provide systems for learning and adaptation,understanding the structure and function of these memory models areimportant. These models are discussed in the figures below.

FIG. 62 shows the interaction process of the three layers of the AISintegrated with the central database management system. The antigen andthe environment (6205) for the evolving antigen (6210-6230) are analyzedat layer 1 (6200) and, if necessary, at layer 2 (6235) and layer 3(6250). Analyses and solutions from layers 2 and 3 are stored in thedbms (6240). The modeling process of layer 3 is shown at 6255-6275.Layer 1 accesses the central database as it discovers an antigen.

FIG. 63 shows the use of the distributed memory in the AIS3. The antigenand the environment (6305) for the evolving antigen (6310-6330) areanalyzed at layer 1 (6300) and, if necessary, at layer 2 (6335) andlayer 3 (6340). Layer 1 accesses dbms 1 (6345), which is informed, inthis distributed model, by information from dbms 2 (6350). Layer 2 isinformed by dbms 2, and receives information from dbms 3 (6355). Layer 3(6340) stores data in and accesses dbms 3 (6355) while producing antigenmodels (6365-6380), including models of a “potential” environment. Thethree layers' databases work together in an integrated way tosuccessively store and access data at critical thresholds of the problemsolving process.

FIG. 64 shows the process of accessing the most recent information frommemory. Layer 2 (6410) stores problem solving information in thedatabase (6420), which is accessed by layer 1 (6400). The numberingsequence at 6420 shows that the most recent information is accessedfirst in memory. Lower priority information is provided a continuouslylower position in the dbms.

FIG. 65 shows the process of solving a problem of an evolving antigen atlayers 1 and 2 using the memory system. After antigen X is discovered(6500) by the system, layer 1 seeks a solution to antigen X by accessingmemory in database (6510). Layer 1 fails to discover a solution andpasses the problem to layer 2 (6520). Layer 2 then seeks to solve theproblem of the new antigen X (6530), solves the eMOOP and stores thesolution in the dbms (6540). The dbms organizes solutions by category(6550) for rapid access. Layer 1 then accesses the dbms to seek andapply a solution to an eMOOP (6560). At each phase of antigen X, thevector evolution is solved by accessing the database (6570). The systemimproves next generation solutions and learns from successive memorysharing processes (6580).

FIG. 66 illustrates how solution candidates are generated at layer 3,tested at layer 2 and applied at layer 2 of the AIS3. Layer 1 (6625)accesses an evolving antigen (6605-6620) in an artificial antigenenvironment (6600) and receives information from a dbms (6655) fromprior stored solutions. The problem is passed on to layer 2, which seeksto experiment with the evolving antigen (6635-6645) in an evolvingenvironment (6630). To analyze the antigen, layer 2 passes the problemto layer 3 (6660), which models multiple scenarios of the antigen aswell as potential antigens (6670-6695). When solution candidates aregenerated at layer 3, they are stored in the dbms as “pre-immunity” andpassed on to layer 2 for testing and experimentation and development ofa solution. Layer 2 passes the solution to the dbms, which is accessedas “imnimunity” by layer 1 when the antigen is later discovered.

FIG. 67 shows the application of solutions generated at layers 2 and 3and the training process that builds immunity. Potential solutions aremodeled at layer 3 by generating scenarios of potential antigens(6743-6750). A promising solution candidate (6747) is tested and storedin the dbms and tested at layer 1 (6725). The solution candidate isapplied to the existing antigen at 6720 and the eMOOP is solved.

FIG. 68 shows the pre-set triggers at specific thresholds of the threelayers that activate the cascade process at layer 1. The process isactivated when the system encounters an evolving antigen (6800) at layer1 (6810). Layer 1 proceeds to analyze the antigen by collectinginformation from past problem-solving experiences at the database. Thisprocess is depicted in 6835 as the multiple antigen analyses proceed at6837-6847, 6849-6865 and 6870-6890. If the problem is solved, forexample at 6865, then the system applies the solution and the systemreturns to equilibrium. However, layer 2 is activated if an adequatesolution is not presented in a specified time. This process of moving toa new level of problem analysis and solution generation involves apre-set trigger that is activated when specific conditions indicate thatlayer 1 is insufficient to solve the problem with existing resources.This triggering mechanism, such as use of preset criteria thatidentifies an aggressive antigen, stimulates a cascade process ofactivating layer 2. Layer 3 is also activated by layer 2 in order toassist in simultaneously modeling the problem and potential problems andgenerating solution candidates.

FIG. 69 shows the process in which layer 3 develops a multi-scenariomodel with specification of conditions to trigger the cascade process oflayer 1, including the possible variables that fit the profile of ahostile new antigen. The antigen modeling at 6940 in layer 3 (6935)identifies the conditions to activate the cascade process of layer 1when, and only when, a specific antigen (6905-6920) is encountered. Thesolution to the antigen is applied at layer 1.

FIG. 70 describes the process of solving an optimization problem usingthe three layers of the AIS3. Once the system encounters antigen T(7000), layer 1 accesses a database and does not find antigen T (7010).Layer 2 is activated to solve the problem of antigen T (7020) and,further, layer 3 models antigen T and develops scenarios to solve theproblem (7030). Layer 3 develops a solution and passes the solution tolayer 1 to solve antigen T, which is stored in a database (7040). Layer1 encounters antigen R which shares attributes of antigen T (7050) andlayer 1 accesses the database and triggers the target window in whichsimilar variables are present (7060). Layer 1 then activates the cascadeprocess to solve antigen R (7070).

FIG. 71 shows the bucket brigade sequence process of passing informationbetween the layers of the AIS3 to solve an optimization problem. Afteran antigen (7105) is encountered at phase 1, layer 2 (7125) accesses adatabase (7140) to obtain information on past antigen identification andsolutions. Layer 1 interacts with the second phase of the development ofthe antigen (7710) by testing a solution and the information it obtainsfrom this phase of the antigen is provided as feedback to layer 1. Ifthe problem is not solved, the system moves to activate layer 2 (7130)at the third phase in the development of the antigen (7115). Informationfrom this phase of the development of the antigen is fed to layer 3(7135) which then models the antigen and solves the problem at 7120. Thesolution is then stored in the dbms.

In FIG. 72, the process of stimulating layers 1 and 2 from layer 3 toperform specific functions is shown. In a later phase of problemsolving, the modeling and analytical functions at layer 3 (7200) providesolutions to be applied at layer 2 (7210) and to layer 1 (7240). Thesolution candidates generated from the models at layer 3 are implementedinto solutions at layer 2 and stored in the database (7230). Layer 1accesses the database to apply solutions to active evolving antigens(7260-7295) in an artificial environment (7250).

FIG. 73 shows how data from layers 1 and 2 are input into layer 3. Afterlayer 1 (7350) encounters an antigen (7300) at phase one, it interactswith applying solutions at the antigen's second phase (7320) byaccessing the database (7360). If layer 1 cannot solve the problem,information about the problem is transmitted to layers 2 (7370) and 3(7380). Layer 2 seeks to solve the problem and enters the solutioncandidates into the database for access to layer 1 interactions with theantigen. Layer 3 models the problem and presents solution candidatesthat are provided to solve the problem (7340), which are stored in thedatabase for future reference.

FIG. 74 shows the process of employing both central memory anddistributed memory in the AIS3. An evolving antigen (7405-7420) isencountered by layer 1 (7425), which accesses dbms 1 (7430) to seek tosolve the problem. If the problem is not solved in layer 1, the systemmoves to solve the problem at layer 2 (7435), which analyzes the problemand stores information in dbms 2 (7440). Dbms 2 provides information tolayer 1 about customized solution candidates to specific new antigens.Layer 2 analyzes the problem and generates solution candidates(7445-7460). Layer 2 further transfers the problem to layer 3 (7465).Layer 3 models various real and artificial synthetic antigen problemsand solution options (7480), which are entered into dbms 3 (7470). Layer3 accesses dbms 3 to model solutions and provides these solutioncandidates to dbms 2, which is later accessed by layer 1 for solutionapplication and experimentation with the antigens. Dbms 1, dbms 2 anddbms 3 each store data in the central database. Information from thecentral database is also accessed by the three distributed databases.Information from the three distributed databases is stored in thecentral dbms at regular intervals.

In FIG. 75, the process is shown of the interaction of the three layersas they store and access data into databases in the AIS3. Once thesystem encounters an antigen (7500), layer 1 accesses dbms 1 and doesnot identify the antigen (7510). Layer 2 seeks to solve the problem andgenerates candidate solutions (7520) and solution options are enteredinto dbms 2 (7530). Layer 3 then models the antigen, develops scenariosof solution candidates and enters solution options into dbms 3 (7540).Databases 1, 2 and 3 are duplicated at the central database at eachphase. Layers 1 and 2 access the central database after each main phaseof storage (7560). Finally, a solution is generated to solve the antigen(7570) and the solution is stored in the central database. In effect,the three distributed databases provide near-term storage to provideheuristic problem solving memory capability as each layer of the systemgenerates analyses and solution candidates to complex eMOOPs.

FIG. 76 shows the parallel operations of layers 1 and 2 and layers 2 and3 to solve two separate problems simultaneously. Layer 1 (7625)interacts with an artificial environment (7600) and encounters theevolving antigen (7605-7620). This first problem is analyzed by layer 1,which accesses dbms 1 (7630) and dbms 2 (7670), and passes the problemto layer 2 (7660). A second problem is encountered at 7640-7655, whichis analyzed at layer 2. If one of the problems is not novel, it iseasily solved by layer 1 after layer 1 accesses dbms 1 for solutions toold problems. If one or both problems are novel, however, then layer 2analyzes both problems simultaneously. Layer 2 passes the problems tolayer 3 (7675) which simultaneously analyzes both problems and generatessolutions for both problems, which are then forwarded to dbms 3 (7680)(which is further forwarded to the central dbms (7690) at regularintervals) and accessed by layer 2. The solution candidates to bothproblems are applied by layer 1 until they are solved (7620 and 7655).Though this description illustrates the use of the AIS3 to solve twoproblems simultaneously, the system may be used to solve many problemssimultaneously contingent on computer resources. The system will solvethe problems in the order of priority and will therefore be in differentstages of problem solving of multiple problems at different times.

FIG. 77 shows the multi-phasal parametric adaptation of the AIS3 inwhich the swelling size of the system configuration at phase two returnsto equilibrium at phase three. At the first phase, the system is inequilibrium as it solves an eMOOP. However, at phase two, the systemgenerates multiple solution options as it models eMOOPs. In this phase,it expands the resource capacity of all the layers functions tointeroperate in order to solve the problems. At phase three, the systemreturns to an equilibrium state. This plasticity behavior of theparametric adaptation of the three layers is contingent on the numberand complexity of the problems introduced to the system. If the problemscontinue to grow, the system will adjust to a new equilibrium withinphase two and not return to a stable equilibrium.

In FIG. 78, the three phases of co-evolutionary plasticity at layer 3,in which growth and decline of activity returns to equilibrium, isillustrated. In phase I, the system is in relative equilibrium as itsolves known problems by applying immunity. However, at phase II, thesystem encounters a novel antigen which requires the modeling analysesof layer 3. As layer 3 expands its computational resource capacity tosolve these problems, which are passed to layer 1 for application, thesystem substantially increases computational demands. As the new problemis solved, the computational resources used at layer 3 are substantiallydiminished in phase III. In this figure, layer 3 is the location forconcentrated activity.

FIG. 79 shows the process of using hybrid genetic algorithms to solveproblems in the AIS3. After the system discovers an antigen (7900),layer 3 models the virus mutation pathway vectors with hybrid geneticalgorithms (HGAs) (7910). HGAs calculate probable future scenarios ofmutation vectors (7920) and identify the most efficient pathway ofmutation vectors (7930). Layer 3 solves the eMOOP by constructing uniquecombinations of antibodies (7940) and the solution is transmitted tolayer 2 (7950). Layer 2 experiments with the solution candidate andtests the solution by interacting with the antigen (7960). The solutionis refined at layer 3 with HGA (7970) and the refined solution istransmitted to layer 1 to solve the eMOOP (7980).

If an antigen is not solved within time constraints, the host may becompromised. As a consequence of this fact, it is necessary to imposetime constraints on solution generation mechanisms of the AIS3. However,one way to achieve solutions within time constraints is to apply fuzzylogic filters in which 100% of the solution of not developed, but rathera majority of the solution is developed within immediate timeconstraints while the remainder of the problem is continuously solvedover time. The limitation of this approach is that the solution is notcompletely solved, which may require future investment of computerresources. However, in the vast majority of cases, solving the problemin a limited way will be sufficient to maximize resource utility. Inorder to apply FL solutions within temporal constraints, the system willapply multiple hybrid metaheuristics at layers 2 and 3.

In FIG. 80, the process is described of solving of an eMOOP within timeconstraints using the three layers of the AIS3. Once the systemencounters an antigen (8000), layer 1 compares the antigen to antigensin the database (8010). If the antigen is identified in the database,the solution is applied (8020). If the antigen is not identified, layer2 is activated to construct a novel solution (8030). The layer 2solution is applied to solve the problem of the antigen within timeconstraints (8040). If the antigen is not solved, layer 3 is activated(8050) and layer 3 modeling solves the eMOOP of the antigen within timeconstraints (8060). The solution is then applied and stored in thedatabase (8070).

There are several categories of engineering applications of the AIS3.These application categories include artificial neural networks, proteinnetwork modeling, and protein structure predictions, network computingand autonomic computing, evolutionary systems and evolvable hardware,robotics systems and networks, and reconfigurable logic devices. Theseapplications are specified below, though this list is not intended to becomplete.

FIG. 81 shows the adaptation process by restructuring connection weightsin an artificial neural network to solve EMOOPs using the AIS3. On theleft column, the AIS3 layers are illustrated to analogize the process ofproblem solving as they restructure connection weights in anevolutionary A-NN in the center column, which tracks eMOOPs, depictedhere in the right column. At phase 1, layer 1 (8100) of the AIS3 detectsthe first phase of the MOOP (8140) and assists the restructuring of theneural network (8115) to optimize the interaction with the antigen inthe environment to solve the problem. At phase 2, the feedback from theeMOOP (8145) requires layer 2 (8105) to analyze the problem and torestructure the A-NN (8120). This process continues as the systemactivates layer 3 (8110) in a later phase to continue to restructure theA-NN (8125, 8130 and 8135) to solve the eMOOP. This process ofcontinuously restructuring A-NN connection weights with the AIS3maximizes the adaptation and learning capabilities of the neural networkas it interacts with the environment to solve the optimization problem.

FIG. 82 shows the use of the AIS3 to train the A-NN connection weights.In the first phase, the A-NN (8215) initiates a configuration based onavailable information to solve a MOOP. The AIS3 is activated at layer 1(8210), which accesses the dbms 1 (8205) for past solutions to currentproblems, though the novel problems are passed to layers 2 (8225) and 3(8240), which update the central database (8200) at regular intervals.As the eMOOP progresses, the A-NN evolves its configuration to solve theproblem, as is shown at 8230 and 8245. At each stage of the process, theAIS3 provides solution candidates that are tested by the A-NN to solvethe eMOOP. The updatable and accumulated memory systems of the AIS3provide a strong metaheuristic to the A-NN to accelerate its solutionsin real time to solve complex optimization problems within time andcomputational constraints.

FIG. 83 shows the process of training connection weights in an A-NNusing the AIS3. After the AIS3 trains the A-NN by solving the problem ofidentifying the connection weight (8300), layer 3 models the problem(8310) and layer 2 develops an original solution to the connectionweight problem in the A-NN (8320). Tentative solutions are tested atlayer 1 (8330) and solutions are improved with feedback from layer 3modeling (8340). AIS3 then applies the solutions to the A-NN (8350). Theenvironment provides feedback to an A-NN (8360) and the AIS3 trains theA-NN by providing constantly updated memory to solve connection weightproblems (8370).

FIG. 84 shows the process of solving a protein regulatory network eMOOPusing the AIS3. Once the protein regulatory network presents an eMOOP(8400), the AIS3 tests protein regulatory network pathway vector options(8410) and layer 3 models the protein network pathway options (8420).Layer 2 generates solution options to eMOOPs (8430) and solution optionsare tested at layer 1 (8440). The AIS3 receives feedback from theprotein network environment (8450) and layer 2 experiments by producingsolution options at layer 1 (8460). The protein regulatory networkeMOOPs are then solved (8470).

FIG. 85 shows the process of solving a protein structure predictioneMOOP using the AIS3. After the AIS3 attempts to solve the proteinstructure prediction eMOOPs (8500), layer 2 generates solution optionsto a protein structure optimization problem (8510) and layer 1 testssolution options from layer 2 (8520). Layer 3 models protein structureoptimal environmental conditions (8530) and the optimal proteinstructure solution option receives feedback from the environment (8540).The system then continuously generates solution options until theproblem is solved. The AIS3 then presents the solution option to theprotein structure prediction problem within constraints (8550) and thesystem responds to adjustments of environmental conditions (8560). Thesystem then repeats until the protein structure prediction model iscomplete.

In FIG. 86 the process of using the AIS3 to model and adapt artificialprotein combinations for synthetic biology is shown. After layer 3models the artificial protein combinations (8600), the artificialorganism is tested at layer 1 (8610). Layer 2 then generates artificialprotein combinations to satisfy optimization constraints (8620), whichare again tested at layer 1. Layer 3 models the reorganization of theprotein combinations (8630) and the artificial organism interacts withits environment (8640). The artificial organism receives feedback fromits environment at layer 1 (8650) and the layer 3 modeling of artificialprotein combinations continues. Ultimately, the artificial organismevolves within environmental constraints (8660).

FIG. 87 shows the process of using the AIS3 to optimize storage pathwaysin a database management system. After data sets are stored in aspatio-temporal object relational (STOR) database management system(dbms) (8700), the AIS3 analyzes the data sets to optimize the storagepathways (8710). The latest information and highest quality informationis analyzed and ranked at a high priority (8720). Layer 1 assesses paststorage pathways and fails to find a solution for a best pathway (8730).Layer 2 analyzes a new stream of data sets and develops novel pathwaysolution (8740), which is then tested at layer 1 (8750). Layer 3 modelspotential data sets and develops potential simulations to test at layer1 (8760). New data sets enter the STOR dbms and are rapidly routedwithin constraints (8770). The AIS3 solves plasticity optimizationproblems of distributed STOR dbms (8780) and the data access process isaccelerated (8790). The AIS3 is also applicable to numerous dbms types.

FIG. 88 shows the process of solving an enterprise resource allocationproblem using the AIS3. Once the enterprise is presented with a resourceallocation problem (8800), the AIS3 analyzes the problem at layer 1 bycomparing the problem to prior problems in the dbms (8810). The problemis unresolved (8820) and layer 2 then analyzes the problem and presentssolution options (8830), which are tested at layer 1 by interacting withthe environment (8840). The resource allocation problem is solved andthe solution stored in the dbms (8850). As the enterprise environmentchanges and presents new problems (8860), the problems are analyzed andsolved at layer 2. Those problems that are not solved at layer 2,however, pass on to layer 3. Layer 3 models the enterprise environmentand generates simulations of optimal resource allocation conditions(8870). When the conditions for the enterprise are sub-optimal, the AIS3solves problems to continuously reallocate resources (8880).

FIG. 89 shows the process of solving an autonomic computing problemusing the AIS3. After the autonomic computing system encounters a newproblem (8900), the layer 1 of the AIS3 diagnoses the problem bycomparing it to a database of known problems (8910). For novel problems,layer 2 analyzes the problem by comparing the attributes to knownproblems (8920). Layer 3 then models the problem and presents scenariosolution options (8930). Layer 2 develops a solution candidate (8940)and layer 1 tests the solution candidate and receives feedback from theenvironment (8950). If the problem is not solved by the solutioncandidates, layers 2 and 3 continue to analyze the problem, and thefeedback to prior solution attempts, to develop further solutioncandidates. Layer 1 tests refined solution candidates (8960) and theAIS3 solves the problem and stores the solution in a dbms (8970).

FIG. 90 shows the process of solving a computer network virus problemusing the AIS3. After the computer network encounters a computer virus(9000), layer 1 compares the computer virus to known viruses in the dbms(9010). If it is novel, layer 2 analyzes the virus and develops solutioncandidates (9020), which are tested at layer 1 by interacting with theenvironment (9030). Layer 3 models the novel virus, the networkenvironment and potential viruses in simulations (9040). A refinedsolution is generated and applied to neutralize the virus and thesolution is stored in a dbms (9050). Once a new virus is detected(9060), the system compares the new virus to viruses in the dbms fromlayer 2 solutions and layer 3 models (9070). The system solves theproblem at layers 2 and 3 by using analytical and modeling tools andneutralizes the new virus (9080) at layer 1.

FIG. 91 shows the process of reorganizing a communications network tosolve a distributed network problem by using the AIS3. After thecommunications network receives data inputs (9100), data sets are routedin the distributed network (9110). Layer 1 of the AIS3 analyzes theoptimal routing pathway by comparing the problem to past problems andsolutions in the dbms (9120). Layer 1 develops and initial schedule forthe optimal routing pathway and receives environmental feedback (9130).The distributed network encounters a new routing problem and performsinefficiently until it is solved (9140). Layer 2 analyzes the networkrouting problem and develops solution candidates (9150). Layer 3 thenmodels the network problem and the network environment and proposessolutions (9160). Solution candidates are tested, refined and applied atlayer 1 and stored in the dbms (9170). The communications networkconstantly reroutes to rebalance the load and engage in a dynamicplasticity process (9180).

FIG. 92 shows the process of reorganizing the structure of an FPGA usingthe AIS3. After the FPGA is organized in a specific configuration(9200), the FPGA receives inputs from the environment (9210). The AIS3analyzes inputs at layer 1 by comparing them to past inputs andsolutions in the dbms (9220). Layer 1 generates and tests solutions byreceiving feedback from the environment and the FGPA reorganizes to anew position (9230). The FPGA receives new inputs from the environment(9240) and layer 2 analyzes new inputs and generates solution optionsthat are tested at layer 1 (9250). Layer 3 models the environment,anticipates potential inputs and develops solution options (9260). Thesesolution options are tested at layer 1 as the system receives feedbackfrom the environment and layers 2 and 3 continue to analyze and modelproblems to generate solution options. The FPGA restructures to newpositions when thresholds are met from recommended solutions from layers2 and 3 (9270). The FPGA continuously restructures from indeterministicenvironment inputs to solve problems as the environment continues tochange (9280).

FIG. 93 shows the process of solving a robot navigation problem usingthe AIS3. After a robot navigates its environment and encounters aproblem (9300), the AIS3 analyzes the problem at layer 1 by comparing itto past problems and solutions in the dbms (9310) and does not find asolution. Layer 2 analyzes the problem and develops solution candidates(9320), which are tested at layer 1 and still do not solve the problem(9330). Layer 3 models the problem and the robot environment toanticipate solution options (9340). When the robot encounters a problem,layer 2 analyzes the evidence that leads to a layer 3 problem solvingsequence in the dbms (9350). The layer 3 solution candidates are testedat layer 1 and refined at layer 2 (9360). The robotic navigation problemis solved (9370), applied and stored in memory for future reference.

FIG. 94 shows the process of solving a collective robotics problem usingthe AIS3. After a group of robots encounters a problem in the roboticenvironment (9400), the robot collective applies the AIS3 to analyze andsolve the problem (9410). Layer 1 accesses the dbms to compare theproblem to prior problems and solutions (9420) and layer 2 then analyzesthe problem and develops solution candidates (9430), which are tested atlayer 1 with environmental feedback (9440). Layer 3 then models therobots' environment and develops potential solutions (9450). The robotsdivide tasks, perform functions and change spatio-temporal position(9460). As the robot collective encounters new problems (9470), therobots rapidly solve the problems by accessing the AIS3 (9480) and theprocess of solving problems repeats. This process is useful in a varietyof robotic collectives, including micro-robotic collectives andnano-robotic collectives, that are useful for reaggregation of evolvablehardware.

LIST OF ACRONYMS

ACO, ant colony optimizationAIS, artificial immune systemAIS3, hybrid multilayer artificial immune systemA-NN, artificial neural networkASIC, application specific integrated circuitBOOP, bi-objective optimization problemCR, collective roboticsEGA, efficient genetic algorithmseMOOP, evolving multi-objective optimization problemFPGA, field programmable gate arrayHGA, hybrid genetic algorithmsHIS, human immune systemMOOP, multi-objective optimization problemPSO, particle swarm optimizationSDS, stochastic diffusion search

1. A system for constructing an artificial immune system (AIS) to solveoptimization problems, comprising: a computer hardware system; acomputer operating system; a computer database management system (dbms);Layer 1 to analyze and solve optimization problems; Layer 2 to analyzeand solve novel optimization problems; Layer 3 to model, simulate andanticipate novel optimization problems; wherein the AIS is generated bythe computer operating system on the computer hardware system byaccessing the dbms to retrieve data to Layer 1 to assist in analysis ofmulti-objective optimization problems (MOOPs) as artificial antigens;wherein the AIS passes to Layer 2 to assist in analysis of novel MOOPsand artificial antigens; wherein the AIS passes to Layer 3 to model thenovel MOOPs and to generate simulations of the artificial antigens;wherein Layer 1 tests solution candidates generated at Layers 2 and 3 byinteracting with the artificial antigen; wherein Layer 1 solves theproblem by generating a unique configuration of a collective ofartificial antibodies to destroy the artificial antigen; wherein Layer 1solves the MOOPs and stores the solutions in the dbms; when the MOOPsare not solved at Layer 1, Layer 2 analysis is activated; and when theMOOPs are not solved at Layer 2, Layer 3 modeling is activated.
 2. Asystem of claim 1, wherein: the optimization problem is an evolvingmulti-objective optimization problem (eMOOP); the eMOOP is representedas an evolving artificial antigen; the AIS analyzes the eMOOP atmultiple phases of its development; and the AIS solves the eMOOP withintime constraints.
 3. A system of claim 1, wherein: layer 2 solves noveleMOOPs by analyzing specific characteristics of the artificial antigenby accessing the dbms and comparing the categories of variables ofmultiple past eMOOP analyses; layer 2 generates customized solutions tosolve a novel eMOOP; and the solution generated at layer 2 is passed tolayer 1 to solve by initiating a cascade a of collective of artificialantibodies.
 4. A system of claim 1, wherein: layer 3 solves novel eMOOPsby generating models of the artificial antigen; layer 3 generatessimulations of the environment for the artificial antigen; layer 3generates simulations of multiple interacting artificial antigens; whenLayer 3 analyzes a model of an artificial antigen, it predicts theantigen's behaviors; layer 3 anticipates the behaviors of evolvingartificial antigens; layer 3 passes these analyses to Layer 2 forsolution candidate generation; layer 2 tests the solution candidates toselect the best available solution option; layer 2 passes the solutionoption to Layer 1; and layer 1 tests the solution by interacting withthe antigen.
 5. A system of claim 1, wherein: the anticipation processby Layer 3 modeling simulations is used by Layer 1 to predict thedevelopmental characteristics of known eMOOPs; the known eMOOPs arerapidly solved at Layer 1; and solutions are stored in the dbms.
 6. Asystem of claim 1, wherein: the anticipation process by Layer 3 modelingsimulations is used by Layer 2 to predict the developmentcharacteristics of novel eMOOPs; the novel eMOOPs are rapidly solved;and solutions are stored in the dbms.
 7. A system of claim 1, wherein:the dbms is a central database; and layers 1, 2 and 3 store data intoand access the central database.
 8. A system of claim 1, wherein: thedbms is a collection of decentralized databases; the database componentsare distributed in multiple locations; layer 1 has a specific dbms 1 inwhich Layer 1 stores and accesses data; layer 2 has a specific dbms 2 inwhich Layer 2 stores and accesses data; and layer 3 has a specific dbms3 in which Layer 3 stores and accesses data.
 9. A system of claim 1,wherein: the dbms is a collection of decentralized databases thatinteracts with a central database; each of the three layers has its owndatabase; the multiple distributed databases access and store data in acentral database; the central database accesses the distributeddatabases; and each of the three distributed databases store data in thecentral database at regular intervals.
 10. A system of claim 1, wherein:the AIS is applied to network computing to solve eMOOPs; the AIS solveseMOOPs involving autonomic computing processes for diagnoses andregulation of computer systems; and the AIS solves eMOOPs involvingcomputer viruses in computer system.
 11. A system of claim 1, wherein:the AIS is applied to solving eMOOPs involving protein structureprediction eMOOPs; and the AIS is applied to solving eMOOPs involvingprotein regulatory network functions within time constraints.
 12. Asystem of claim 1, wherein: the AIS is applied to solving artificialneural networks eMOOPs; and the artificial neural networks eMOOPsinvolve calculating connection weights within time constraints.
 13. Asystem of claim 1, wherein: the AIS is applied to solving eMOOPsinvolving evolutionary computation; and the AIS solves eMOOPs forindeterministic continuously programmable FPGAs in an uncertainenvironment.
 14. A system of claim 1, wherein: the AIS is applied tosolving eMOOPs involving robot navigation; and the AIS solves robotnavigation eMOOPs in uncertain environments within time constraints. 15.A system of claim 1, wherein: the AIS is applied to solving eMOOPsinvolving collectives of robots; the AIS solves collective roboticsproblems involving robot networking interactions; and the AIS solvescollective robotics problems involving navigation of specific robotperformance in uncertain environments within time constraints.
 16. Asystem of claim 1, wherein: the AIS is applied to solving eMOOPsinvolving evolvable hardware of collectives of nanorobots; the AISsolves collective nano-robotic problems involving the aggregation andreaggregation of system configurations in uncertain environments withintime constraints; and the collective of nano-robots is used inbiological and medical applications.